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• v.17(1); Spring 2018

Understanding the Complex Relationship between Critical Thinking and Science Reasoning among Undergraduate Thesis Writers

Jason e. dowd.

† Department of Biology, Duke University, Durham, NC 27708

Robert J. Thompson, Jr.

‡ Department of Psychology and Neuroscience, Duke University, Durham, NC 27708

Leslie A. Schiff

§ Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN 55455

Julie A. Reynolds

Associated data.

This study empirically examines the relationship between students’ critical-thinking skills and scientific reasoning as reflected in undergraduate thesis writing in biology. Writing offers a unique window into studying this relationship, and the findings raise potential implications for instruction.

Developing critical-thinking and scientific reasoning skills are core learning objectives of science education, but little empirical evidence exists regarding the interrelationships between these constructs. Writing effectively fosters students’ development of these constructs, and it offers a unique window into studying how they relate. In this study of undergraduate thesis writing in biology at two universities, we examine how scientific reasoning exhibited in writing (assessed using the Biology Thesis Assessment Protocol) relates to general and specific critical-thinking skills (assessed using the California Critical Thinking Skills Test), and we consider implications for instruction. We find that scientific reasoning in writing is strongly related to inference , while other aspects of science reasoning that emerge in writing (epistemological considerations, writing conventions, etc.) are not significantly related to critical-thinking skills. Science reasoning in writing is not merely a proxy for critical thinking. In linking features of students’ writing to their critical-thinking skills, this study 1) provides a bridge to prior work suggesting that engagement in science writing enhances critical thinking and 2) serves as a foundational step for subsequently determining whether instruction focused explicitly on developing critical-thinking skills (particularly inference ) can actually improve students’ scientific reasoning in their writing.

INTRODUCTION

Critical-thinking and scientific reasoning skills are core learning objectives of science education for all students, regardless of whether or not they intend to pursue a career in science or engineering. Consistent with the view of learning as construction of understanding and meaning ( National Research Council, 2000 ), the pedagogical practice of writing has been found to be effective not only in fostering the development of students’ conceptual and procedural knowledge ( Gerdeman et al. , 2007 ) and communication skills ( Clase et al. , 2010 ), but also scientific reasoning ( Reynolds et al. , 2012 ) and critical-thinking skills ( Quitadamo and Kurtz, 2007 ).

Critical thinking and scientific reasoning are similar but different constructs that include various types of higher-order cognitive processes, metacognitive strategies, and dispositions involved in making meaning of information. Critical thinking is generally understood as the broader construct ( Holyoak and Morrison, 2005 ), comprising an array of cognitive processes and dispostions that are drawn upon differentially in everyday life and across domains of inquiry such as the natural sciences, social sciences, and humanities. Scientific reasoning, then, may be interpreted as the subset of critical-thinking skills (cognitive and metacognitive processes and dispositions) that 1) are involved in making meaning of information in scientific domains and 2) support the epistemological commitment to scientific methodology and paradigm(s).

Although there has been an enduring focus in higher education on promoting critical thinking and reasoning as general or “transferable” skills, research evidence provides increasing support for the view that reasoning and critical thinking are also situational or domain specific ( Beyer et al. , 2013 ). Some researchers, such as Lawson (2010) , present frameworks in which science reasoning is characterized explicitly in terms of critical-thinking skills. There are, however, limited coherent frameworks and empirical evidence regarding either the general or domain-specific interrelationships of scientific reasoning, as it is most broadly defined, and critical-thinking skills.

The Vision and Change in Undergraduate Biology Education Initiative provides a framework for thinking about these constructs and their interrelationship in the context of the core competencies and disciplinary practice they describe ( American Association for the Advancement of Science, 2011 ). These learning objectives aim for undergraduates to “understand the process of science, the interdisciplinary nature of the new biology and how science is closely integrated within society; be competent in communication and collaboration; have quantitative competency and a basic ability to interpret data; and have some experience with modeling, simulation and computational and systems level approaches as well as with using large databases” ( Woodin et al. , 2010 , pp. 71–72). This framework makes clear that science reasoning and critical-thinking skills play key roles in major learning outcomes; for example, “understanding the process of science” requires students to engage in (and be metacognitive about) scientific reasoning, and having the “ability to interpret data” requires critical-thinking skills. To help students better achieve these core competencies, we must better understand the interrelationships of their composite parts. Thus, the next step is to determine which specific critical-thinking skills are drawn upon when students engage in science reasoning in general and with regard to the particular scientific domain being studied. Such a determination could be applied to improve science education for both majors and nonmajors through pedagogical approaches that foster critical-thinking skills that are most relevant to science reasoning.

Writing affords one of the most effective means for making thinking visible ( Reynolds et al. , 2012 ) and learning how to “think like” and “write like” disciplinary experts ( Meizlish et al. , 2013 ). As a result, student writing affords the opportunities to both foster and examine the interrelationship of scientific reasoning and critical-thinking skills within and across disciplinary contexts. The purpose of this study was to better understand the relationship between students’ critical-thinking skills and scientific reasoning skills as reflected in the genre of undergraduate thesis writing in biology departments at two research universities, the University of Minnesota and Duke University.

In the following subsections, we discuss in greater detail the constructs of scientific reasoning and critical thinking, as well as the assessment of scientific reasoning in students’ thesis writing. In subsequent sections, we discuss our study design, findings, and the implications for enhancing educational practices.

Critical Thinking

The advances in cognitive science in the 21st century have increased our understanding of the mental processes involved in thinking and reasoning, as well as memory, learning, and problem solving. Critical thinking is understood to include both a cognitive dimension and a disposition dimension (e.g., reflective thinking) and is defined as “purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considera­tions upon which that judgment is based” ( Facione, 1990, p. 3 ). Although various other definitions of critical thinking have been proposed, researchers have generally coalesced on this consensus: expert view ( Blattner and Frazier, 2002 ; Condon and Kelly-Riley, 2004 ; Bissell and Lemons, 2006 ; Quitadamo and Kurtz, 2007 ) and the corresponding measures of critical-­thinking skills ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ).

Both the cognitive skills and dispositional components of critical thinking have been recognized as important to science education ( Quitadamo and Kurtz, 2007 ). Empirical research demonstrates that specific pedagogical practices in science courses are effective in fostering students’ critical-thinking skills. Quitadamo and Kurtz (2007) found that students who engaged in a laboratory writing component in the context of a general education biology course significantly improved their overall critical-thinking skills (and their analytical and inference skills, in particular), whereas students engaged in a traditional quiz-based laboratory did not improve their critical-thinking skills. In related work, Quitadamo et al. (2008) found that a community-based inquiry experience, involving inquiry, writing, research, and analysis, was associated with improved critical thinking in a biology course for nonmajors, compared with traditionally taught sections. In both studies, students who exhibited stronger presemester critical-thinking skills exhibited stronger gains, suggesting that “students who have not been explicitly taught how to think critically may not reach the same potential as peers who have been taught these skills” ( Quitadamo and Kurtz, 2007 , p. 151).

Recently, Stephenson and Sadler-McKnight (2016) found that first-year general chemistry students who engaged in a science writing heuristic laboratory, which is an inquiry-based, writing-to-learn approach to instruction ( Hand and Keys, 1999 ), had significantly greater gains in total critical-thinking scores than students who received traditional laboratory instruction. Each of the four components—inquiry, writing, collaboration, and reflection—have been linked to critical thinking ( Stephenson and Sadler-McKnight, 2016 ). Like the other studies, this work highlights the value of targeting critical-thinking skills and the effectiveness of an inquiry-based, writing-to-learn approach to enhance critical thinking. Across studies, authors advocate adopting critical thinking as the course framework ( Pukkila, 2004 ) and developing explicit examples of how critical thinking relates to the scientific method ( Miri et al. , 2007 ).

In these examples, the important connection between writing and critical thinking is highlighted by the fact that each intervention involves the incorporation of writing into science, technology, engineering, and mathematics education (either alone or in combination with other pedagogical practices). However, critical-thinking skills are not always the primary learning outcome; in some contexts, scientific reasoning is the primary outcome that is assessed.

Scientific Reasoning

Scientific reasoning is a complex process that is broadly defined as “the skills involved in inquiry, experimentation, evidence evaluation, and inference that are done in the service of conceptual change or scientific understanding” ( Zimmerman, 2007 , p. 172). Scientific reasoning is understood to include both conceptual knowledge and the cognitive processes involved with generation of hypotheses (i.e., inductive processes involved in the generation of hypotheses and the deductive processes used in the testing of hypotheses), experimentation strategies, and evidence evaluation strategies. These dimensions are interrelated, in that “experimentation and inference strategies are selected based on prior conceptual knowledge of the domain” ( Zimmerman, 2000 , p. 139). Furthermore, conceptual and procedural knowledge and cognitive process dimensions can be general and domain specific (or discipline specific).

With regard to conceptual knowledge, attention has been focused on the acquisition of core methodological concepts fundamental to scientists’ causal reasoning and metacognitive distancing (or decontextualized thinking), which is the ability to reason independently of prior knowledge or beliefs ( Greenhoot et al. , 2004 ). The latter involves what Kuhn and Dean (2004) refer to as the coordination of theory and evidence, which requires that one question existing theories (i.e., prior knowledge and beliefs), seek contradictory evidence, eliminate alternative explanations, and revise one’s prior beliefs in the face of contradictory evidence. Kuhn and colleagues (2008) further elaborate that scientific thinking requires “a mature understanding of the epistemological foundations of science, recognizing scientific knowledge as constructed by humans rather than simply discovered in the world,” and “the ability to engage in skilled argumentation in the scientific domain, with an appreciation of argumentation as entailing the coordination of theory and evidence” ( Kuhn et al. , 2008 , p. 435). “This approach to scientific reasoning not only highlights the skills of generating and evaluating evidence-based inferences, but also encompasses epistemological appreciation of the functions of evidence and theory” ( Ding et al. , 2016 , p. 616). Evaluating evidence-based inferences involves epistemic cognition, which Moshman (2015) defines as the subset of metacognition that is concerned with justification, truth, and associated forms of reasoning. Epistemic cognition is both general and domain specific (or discipline specific; Moshman, 2015 ).

There is empirical support for the contributions of both prior knowledge and an understanding of the epistemological foundations of science to scientific reasoning. In a study of undergraduate science students, advanced scientific reasoning was most often accompanied by accurate prior knowledge as well as sophisticated epistemological commitments; additionally, for students who had comparable levels of prior knowledge, skillful reasoning was associated with a strong epistemological commitment to the consistency of theory with evidence ( Zeineddin and Abd-El-Khalick, 2010 ). These findings highlight the importance of the need for instructional activities that intentionally help learners develop sophisticated epistemological commitments focused on the nature of knowledge and the role of evidence in supporting knowledge claims ( Zeineddin and Abd-El-Khalick, 2010 ).

Scientific Reasoning in Students’ Thesis Writing

Pedagogical approaches that incorporate writing have also focused on enhancing scientific reasoning. Many rubrics have been developed to assess aspects of scientific reasoning in written artifacts. For example, Timmerman and colleagues (2011) , in the course of describing their own rubric for assessing scientific reasoning, highlight several examples of scientific reasoning assessment criteria ( Haaga, 1993 ; Tariq et al. , 1998 ; Topping et al. , 2000 ; Kelly and Takao, 2002 ; Halonen et al. , 2003 ; Willison and O’Regan, 2007 ).

At both the University of Minnesota and Duke University, we have focused on the genre of the undergraduate honors thesis as the rhetorical context in which to study and improve students’ scientific reasoning and writing. We view the process of writing an undergraduate honors thesis as a form of professional development in the sciences (i.e., a way of engaging students in the practices of a community of discourse). We have found that structured courses designed to scaffold the thesis-­writing process and promote metacognition can improve writing and reasoning skills in biology, chemistry, and economics ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In the context of this prior work, we have defined scientific reasoning in writing as the emergent, underlying construct measured across distinct aspects of students’ written discussion of independent research in their undergraduate theses.

The Biology Thesis Assessment Protocol (BioTAP) was developed at Duke University as a tool for systematically guiding students and faculty through a “draft–feedback–revision” writing process, modeled after professional scientific peer-review processes ( Reynolds et al. , 2009 ). BioTAP includes activities and worksheets that allow students to engage in critical peer review and provides detailed descriptions, presented as rubrics, of the questions (i.e., dimensions, shown in Table 1 ) upon which such review should focus. Nine rubric dimensions focus on communication to the broader scientific community, and four rubric dimensions focus on the accuracy and appropriateness of the research. These rubric dimensions provide criteria by which the thesis is assessed, and therefore allow BioTAP to be used as an assessment tool as well as a teaching resource ( Reynolds et al. , 2009 ). Full details are available at www.science-writing.org/biotap.html .

Theses assessment protocol dimensions

In previous work, we have used BioTAP to quantitatively assess students’ undergraduate honors theses and explore the relationship between thesis-writing courses (or specific interventions within the courses) and the strength of students’ science reasoning in writing across different science disciplines: biology ( Reynolds and Thompson, 2011 ); chemistry ( Dowd et al. , 2015b ); and economics ( Dowd et al. , 2015a ). We have focused exclusively on the nine dimensions related to reasoning and writing (questions 1–9), as the other four dimensions (questions 10–13) require topic-specific expertise and are intended to be used by the student’s thesis supervisor.

Beyond considering individual dimensions, we have investigated whether meaningful constructs underlie students’ thesis scores. We conducted exploratory factor analysis of students’ theses in biology, economics, and chemistry and found one dominant underlying factor in each discipline; we termed the factor “scientific reasoning in writing” ( Dowd et al. , 2015a , b , 2016 ). That is, each of the nine dimensions could be understood as reflecting, in different ways and to different degrees, the construct of scientific reasoning in writing. The findings indicated evidence of both general and discipline-specific components to scientific reasoning in writing that relate to epistemic beliefs and paradigms, in keeping with broader ideas about science reasoning discussed earlier. Specifically, scientific reasoning in writing is more strongly associated with formulating a compelling argument for the significance of the research in the context of current literature in biology, making meaning regarding the implications of the findings in chemistry, and providing an organizational framework for interpreting the thesis in economics. We suggested that instruction, whether occurring in writing studios or in writing courses to facilitate thesis preparation, should attend to both components.

Research Question and Study Design

The genre of thesis writing combines the pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-­McKnight, 2016 ). However, there is no empirical evidence regarding the general or domain-specific interrelationships of scientific reasoning and critical-thinking skills, particularly in the rhetorical context of the undergraduate thesis. The BioTAP studies discussed earlier indicate that the rubric-based assessment produces evidence of scientific reasoning in the undergraduate thesis, but it was not designed to foster or measure critical thinking. The current study was undertaken to address the research question: How are students’ critical-thinking skills related to scientific reasoning as reflected in the genre of undergraduate thesis writing in biology? Determining these interrelationships could guide efforts to enhance students’ scientific reasoning and writing skills through focusing instruction on specific critical-thinking skills as well as disciplinary conventions.

To address this research question, we focused on undergraduate thesis writers in biology courses at two institutions, Duke University and the University of Minnesota, and examined the extent to which students’ scientific reasoning in writing, assessed in the undergraduate thesis using BioTAP, corresponds to students’ critical-thinking skills, assessed using the California Critical Thinking Skills Test (CCTST; August, 2016 ).

Study Sample

The study sample was composed of students enrolled in courses designed to scaffold the thesis-writing process in the Department of Biology at Duke University and the College of Biological Sciences at the University of Minnesota. Both courses complement students’ individual work with research advisors. The course is required for thesis writers at the University of Minnesota and optional for writers at Duke University. Not all students are required to complete a thesis, though it is required for students to graduate with honors; at the University of Minnesota, such students are enrolled in an honors program within the college. In total, 28 students were enrolled in the course at Duke University and 44 students were enrolled in the course at the University of Minnesota. Of those students, two students did not consent to participate in the study; additionally, five students did not validly complete the CCTST (i.e., attempted fewer than 60% of items or completed the test in less than 15 minutes). Thus, our overall rate of valid participation is 90%, with 27 students from Duke University and 38 students from the University of Minnesota. We found no statistically significant differences in thesis assessment between students with valid CCTST scores and invalid CCTST scores. Therefore, we focus on the 65 students who consented to participate and for whom we have complete and valid data in most of this study. Additionally, in asking students for their consent to participate, we allowed them to choose whether to provide or decline access to academic and demographic background data. Of the 65 students who consented to participate, 52 students granted access to such data. Therefore, for additional analyses involving academic and background data, we focus on the 52 students who consented. We note that the 13 students who participated but declined to share additional data performed slightly lower on the CCTST than the 52 others (perhaps suggesting that they differ by other measures, but we cannot determine this with certainty). Among the 52 students, 60% identified as female and 10% identified as being from underrepresented ethnicities.

In both courses, students completed the CCTST online, either in class or on their own, late in the Spring 2016 semester. This is the same assessment that was used in prior studies of critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). It is “an objective measure of the core reasoning skills needed for reflective decision making concerning what to believe or what to do” ( Insight Assessment, 2016a ). In the test, students are asked to read and consider information as they answer multiple-choice questions. The questions are intended to be appropriate for all users, so there is no expectation of prior disciplinary knowledge in biology (or any other subject). Although actual test items are protected, sample items are available on the Insight Assessment website ( Insight Assessment, 2016b ). We have included one sample item in the Supplemental Material.

The CCTST is based on a consensus definition of critical thinking, measures cognitive and metacognitive skills associated with critical thinking, and has been evaluated for validity and reliability at the college level ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ). In addition to providing overall critical-thinking score, the CCTST assesses seven dimensions of critical thinking: analysis, interpretation, inference, evaluation, explanation, induction, and deduction. Scores on each dimension are calculated based on students’ performance on items related to that dimension. Analysis focuses on identifying assumptions, reasons, and claims and examining how they interact to form arguments. Interpretation, related to analysis, focuses on determining the precise meaning and significance of information. Inference focuses on drawing conclusions from reasons and evidence. Evaluation focuses on assessing the credibility of sources of information and claims they make. Explanation, related to evaluation, focuses on describing the evidence, assumptions, or rationale for beliefs and conclusions. Induction focuses on drawing inferences about what is probably true based on evidence. Deduction focuses on drawing conclusions about what must be true when the context completely determines the outcome. These are not independent dimensions; the fact that they are related supports their collective interpretation as critical thinking. Together, the CCTST dimensions provide a basis for evaluating students’ overall strength in using reasoning to form reflective judgments about what to believe or what to do ( August, 2016 ). Each of the seven dimensions and the overall CCTST score are measured on a scale of 0–100, where higher scores indicate superior performance. Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and below) skills.

Scientific Reasoning in Writing

At the end of the semester, students’ final, submitted undergraduate theses were assessed using BioTAP, which consists of nine rubric dimensions that focus on communication to the broader scientific community and four additional dimensions that focus on the exhibition of topic-specific expertise ( Reynolds et al. , 2009 ). These dimensions, framed as questions, are displayed in Table 1 .

Student theses were assessed on questions 1–9 of BioTAP using the same procedures described in previous studies ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In this study, six raters were trained in the valid, reliable use of BioTAP rubrics. Each dimension was rated on a five-point scale: 1 indicates the dimension is missing, incomplete, or below acceptable standards; 3 indicates that the dimension is adequate but not exhibiting mastery; and 5 indicates that the dimension is excellent and exhibits mastery (intermediate ratings of 2 and 4 are appropriate when different parts of the thesis make a single category challenging). After training, two raters independently assessed each thesis and then discussed their independent ratings with one another to form a consensus rating. The consensus score is not an average score, but rather an agreed-upon, discussion-based score. On a five-point scale, raters independently assessed dimensions to be within 1 point of each other 82.4% of the time before discussion and formed consensus ratings 100% of the time after discussion.

In this study, we consider both categorical (mastery/nonmastery, where a score of 5 corresponds to mastery) and numerical treatments of individual BioTAP scores to better relate the manifestation of critical thinking in BioTAP assessment to all of the prior studies. For comprehensive/cumulative measures of BioTAP, we focus on the partial sum of questions 1–5, as these questions relate to higher-order scientific reasoning (whereas questions 6–9 relate to mid- and lower-order writing mechanics [ Reynolds et al. , 2009 ]), and the factor scores (i.e., numerical representations of the extent to which each student exhibits the underlying factor), which are calculated from the factor loadings published by Dowd et al. (2016) . We do not focus on questions 6–9 individually in statistical analyses, because we do not expect critical-thinking skills to relate to mid- and lower-order writing skills.

The final, submitted thesis reflects the student’s writing, the student’s scientific reasoning, the quality of feedback provided to the student by peers and mentors, and the student’s ability to incorporate that feedback into his or her work. Therefore, our assessment is not the same as an assessment of unpolished, unrevised samples of students’ written work. While one might imagine that such an unpolished sample may be more strongly correlated with critical-thinking skills measured by the CCTST, we argue that the complete, submitted thesis, assessed using BioTAP, is ultimately a more appropriate reflection of how students exhibit science reasoning in the scientific community.

Statistical Analyses

We took several steps to analyze the collected data. First, to provide context for subsequent interpretations, we generated descriptive statistics for the CCTST scores of the participants based on the norms for undergraduate CCTST test takers. To determine the strength of relationships among CCTST dimensions (including overall score) and the BioTAP dimensions, partial-sum score (questions 1–5), and factor score, we calculated Pearson’s correlations for each pair of measures. To examine whether falling on one side of the nonmastery/mastery threshold (as opposed to a linear scale of performance) was related to critical thinking, we grouped BioTAP dimensions into categories (mastery/nonmastery) and conducted Student’s t tests to compare the means scores of the two groups on each of the seven dimensions and overall score of the CCTST. Finally, for the strongest relationship that emerged, we included additional academic and background variables as covariates in multiple linear-regression analysis to explore questions about how much observed relationships between critical-thinking skills and science reasoning in writing might be explained by variation in these other factors.

Although BioTAP scores represent discreet, ordinal bins, the five-point scale is intended to capture an underlying continuous construct (from inadequate to exhibiting mastery). It has been argued that five categories is an appropriate cutoff for treating ordinal variables as pseudo-continuous ( Rhemtulla et al. , 2012 )—and therefore using continuous-variable statistical methods (e.g., Pearson’s correlations)—as long as the underlying assumption that ordinal scores are linearly distributed is valid. Although we have no way to statistically test this assumption, we interpret adequate scores to be approximately halfway between inadequate and mastery scores, resulting in a linear scale. In part because this assumption is subject to disagreement, we also consider and interpret a categorical (mastery/nonmastery) treatment of BioTAP variables.

We corrected for multiple comparisons using the Holm-Bonferroni method ( Holm, 1979 ). At the most general level, where we consider the single, comprehensive measures for BioTAP (partial-sum and factor score) and the CCTST (overall score), there is no need to correct for multiple comparisons, because the multiple, individual dimensions are collapsed into single dimensions. When we considered individual CCTST dimensions in relation to comprehensive measures for BioTAP, we accounted for seven comparisons; similarly, when we considered individual dimensions of BioTAP in relation to overall CCTST score, we accounted for five comparisons. When all seven CCTST and five BioTAP dimensions were examined individually and without prior knowledge, we accounted for 35 comparisons; such a rigorous threshold is likely to reject weak and moderate relationships, but it is appropriate if there are no specific pre-existing hypotheses. All p values are presented in tables for complete transparency, and we carefully consider the implications of our interpretation of these data in the Discussion section.

CCTST scores for students in this sample ranged from the 39th to 99th percentile of the general population of undergraduate CCTST test takers (mean percentile = 84.3, median = 85th percentile; Table 2 ); these percentiles reflect overall scores that range from moderate to superior. Scores on individual dimensions and overall scores were sufficiently normal and far enough from the ceiling of the scale to justify subsequent statistical analyses.

Descriptive statistics of CCTST dimensions a

a Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and lower) skills.

The Pearson’s correlations between students’ cumulative scores on BioTAP (the factor score based on loadings published by Dowd et al. , 2016 , and the partial sum of scores on questions 1–5) and students’ overall scores on the CCTST are presented in Table 3 . We found that the partial-sum measure of BioTAP was significantly related to the overall measure of critical thinking ( r = 0.27, p = 0.03), while the BioTAP factor score was marginally related to overall CCTST ( r = 0.24, p = 0.05). When we looked at relationships between comprehensive BioTAP measures and scores for individual dimensions of the CCTST ( Table 3 ), we found significant positive correlations between the both BioTAP partial-sum and factor scores and CCTST inference ( r = 0.45, p < 0.001, and r = 0.41, p < 0.001, respectively). Although some other relationships have p values below 0.05 (e.g., the correlations between BioTAP partial-sum scores and CCTST induction and interpretation scores), they are not significant when we correct for multiple comparisons.

Correlations between dimensions of CCTST and dimensions of BioTAP a

a In each cell, the top number is the correlation, and the bottom, italicized number is the associated p value. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

b This is the partial sum of BioTAP scores on questions 1–5.

c This is the factor score calculated from factor loadings published by Dowd et al. (2016) .

When we expanded comparisons to include all 35 potential correlations among individual BioTAP and CCTST dimensions—and, accordingly, corrected for 35 comparisons—we did not find any additional statistically significant relationships. The Pearson’s correlations between students’ scores on each dimension of BioTAP and students’ scores on each dimension of the CCTST range from −0.11 to 0.35 ( Table 3 ); although the relationship between discussion of implications (BioTAP question 5) and inference appears to be relatively large ( r = 0.35), it is not significant ( p = 0.005; the Holm-Bonferroni cutoff is 0.00143). We found no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions (unpublished data), regardless of whether we correct for multiple comparisons.

The results of Student’s t tests comparing scores on each dimension of the CCTST of students who exhibit mastery with those of students who do not exhibit mastery on each dimension of BioTAP are presented in Table 4 . Focusing first on the overall CCTST scores, we found that the difference between those who exhibit mastery and those who do not in discussing implications of results (BioTAP question 5) is statistically significant ( t = 2.73, p = 0.008, d = 0.71). When we expanded t tests to include all 35 comparisons—and, like above, corrected for 35 comparisons—we found a significant difference in inference scores between students who exhibit mastery on question 5 and students who do not ( t = 3.41, p = 0.0012, d = 0.88), as well as a marginally significant difference in these students’ induction scores ( t = 3.26, p = 0.0018, d = 0.84; the Holm-Bonferroni cutoff is p = 0.00147). Cohen’s d effect sizes, which reveal the strength of the differences for statistically significant relationships, range from 0.71 to 0.88.

The t statistics and effect sizes of differences in ­dimensions of CCTST across dimensions of BioTAP a

a In each cell, the top number is the t statistic for each comparison, and the middle, italicized number is the associated p value. The bottom number is the effect size. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

Finally, we more closely examined the strongest relationship that we observed, which was between the CCTST dimension of inference and the BioTAP partial-sum composite score (shown in Table 3 ), using multiple regression analysis ( Table 5 ). Focusing on the 52 students for whom we have background information, we looked at the simple relationship between BioTAP and inference (model 1), a robust background model including multiple covariates that one might expect to explain some part of the variation in BioTAP (model 2), and a combined model including all variables (model 3). As model 3 shows, the covariates explain very little variation in BioTAP scores, and the relationship between inference and BioTAP persists even in the presence of all of the covariates.

Partial sum (questions 1–5) of BioTAP scores ( n = 52)

** p < 0.01.

*** p < 0.001.

The aim of this study was to examine the extent to which the various components of scientific reasoning—manifested in writing in the genre of undergraduate thesis and assessed using BioTAP—draw on general and specific critical-thinking skills (assessed using CCTST) and to consider the implications for educational practices. Although science reasoning involves critical-thinking skills, it also relates to conceptual knowledge and the epistemological foundations of science disciplines ( Kuhn et al. , 2008 ). Moreover, science reasoning in writing , captured in students’ undergraduate theses, reflects habits, conventions, and the incorporation of feedback that may alter evidence of individuals’ critical-thinking skills. Our findings, however, provide empirical evidence that cumulative measures of science reasoning in writing are nonetheless related to students’ overall critical-thinking skills ( Table 3 ). The particularly significant roles of inference skills ( Table 3 ) and the discussion of implications of results (BioTAP question 5; Table 4 ) provide a basis for more specific ideas about how these constructs relate to one another and what educational interventions may have the most success in fostering these skills.

Our results build on previous findings. The genre of thesis writing combines pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). Quitadamo and Kurtz (2007) reported that students who engaged in a laboratory writing component in a general education biology course significantly improved their inference and analysis skills, and Quitadamo and colleagues (2008) found that participation in a community-based inquiry biology course (that included a writing component) was associated with significant gains in students’ inference and evaluation skills. The shared focus on inference is noteworthy, because these prior studies actually differ from the current study; the former considered critical-­thinking skills as the primary learning outcome of writing-­focused interventions, whereas the latter focused on emergent links between two learning outcomes (science reasoning in writing and critical thinking). In other words, inference skills are impacted by writing as well as manifested in writing.

Inference focuses on drawing conclusions from argument and evidence. According to the consensus definition of critical thinking, the specific skill of inference includes several processes: querying evidence, conjecturing alternatives, and drawing conclusions. All of these activities are central to the independent research at the core of writing an undergraduate thesis. Indeed, a critical part of what we call “science reasoning in writing” might be characterized as a measure of students’ ability to infer and make meaning of information and findings. Because the cumulative BioTAP measures distill underlying similarities and, to an extent, suppress unique aspects of individual dimensions, we argue that it is appropriate to relate inference to scientific reasoning in writing . Even when we control for other potentially relevant background characteristics, the relationship is strong ( Table 5 ).

In taking the complementary view and focusing on BioTAP, when we compared students who exhibit mastery with those who do not, we found that the specific dimension of “discussing the implications of results” (question 5) differentiates students’ performance on several critical-thinking skills. To achieve mastery on this dimension, students must make connections between their results and other published studies and discuss the future directions of the research; in short, they must demonstrate an understanding of the bigger picture. The specific relationship between question 5 and inference is the strongest observed among all individual comparisons. Altogether, perhaps more than any other BioTAP dimension, this aspect of students’ writing provides a clear view of the role of students’ critical-thinking skills (particularly inference and, marginally, induction) in science reasoning.

While inference and discussion of implications emerge as particularly strongly related dimensions in this work, we note that the strongest contribution to “science reasoning in writing in biology,” as determined through exploratory factor analysis, is “argument for the significance of research” (BioTAP question 2, not question 5; Dowd et al. , 2016 ). Question 2 is not clearly related to critical-thinking skills. These findings are not contradictory, but rather suggest that the epistemological and disciplinary-specific aspects of science reasoning that emerge in writing through BioTAP are not completely aligned with aspects related to critical thinking. In other words, science reasoning in writing is not simply a proxy for those critical-thinking skills that play a role in science reasoning.

In a similar vein, the content-related, epistemological aspects of science reasoning, as well as the conventions associated with writing the undergraduate thesis (including feedback from peers and revision), may explain the lack of significant relationships between some science reasoning dimensions and some critical-thinking skills that might otherwise seem counterintuitive (e.g., BioTAP question 2, which relates to making an argument, and the critical-thinking skill of argument). It is possible that an individual’s critical-thinking skills may explain some variation in a particular BioTAP dimension, but other aspects of science reasoning and practice exert much stronger influence. Although these relationships do not emerge in our analyses, the lack of significant correlation does not mean that there is definitively no correlation. Correcting for multiple comparisons suppresses type 1 error at the expense of exacerbating type 2 error, which, combined with the limited sample size, constrains statistical power and makes weak relationships more difficult to detect. Ultimately, though, the relationships that do emerge highlight places where individuals’ distinct critical-thinking skills emerge most coherently in thesis assessment, which is why we are particularly interested in unpacking those relationships.

We recognize that, because only honors students submit theses at these institutions, this study sample is composed of a selective subset of the larger population of biology majors. Although this is an inherent limitation of focusing on thesis writing, links between our findings and results of other studies (with different populations) suggest that observed relationships may occur more broadly. The goal of improved science reasoning and critical thinking is shared among all biology majors, particularly those engaged in capstone research experiences. So while the implications of this work most directly apply to honors thesis writers, we provisionally suggest that all students could benefit from further study of them.

There are several important implications of this study for science education practices. Students’ inference skills relate to the understanding and effective application of scientific content. The fact that we find no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions suggests that such mid- to lower-order elements of BioTAP ( Reynolds et al. , 2009 ), which tend to be more structural in nature, do not focus on aspects of the finished thesis that draw strongly on critical thinking. In keeping with prior analyses ( Reynolds and Thompson, 2011 ; Dowd et al. , 2016 ), these findings further reinforce the notion that disciplinary instructors, who are most capable of teaching and assessing scientific reasoning and perhaps least interested in the more mechanical aspects of writing, may nonetheless be best suited to effectively model and assess students’ writing.

The goal of the thesis writing course at both Duke University and the University of Minnesota is not merely to improve thesis scores but to move students’ writing into the category of mastery across BioTAP dimensions. Recognizing that students with differing critical-thinking skills (particularly inference) are more or less likely to achieve mastery in the undergraduate thesis (particularly in discussing implications [question 5]) is important for developing and testing targeted pedagogical interventions to improve learning outcomes for all students.

The competencies characterized by the Vision and Change in Undergraduate Biology Education Initiative provide a general framework for recognizing that science reasoning and critical-thinking skills play key roles in major learning outcomes of science education. Our findings highlight places where science reasoning–related competencies (like “understanding the process of science”) connect to critical-thinking skills and places where critical thinking–related competencies might be manifested in scientific products (such as the ability to discuss implications in scientific writing). We encourage broader efforts to build empirical connections between competencies and pedagogical practices to further improve science education.

One specific implication of this work for science education is to focus on providing opportunities for students to develop their critical-thinking skills (particularly inference). Of course, as this correlational study is not designed to test causality, we do not claim that enhancing students’ inference skills will improve science reasoning in writing. However, as prior work shows that science writing activities influence students’ inference skills ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ), there is reason to test such a hypothesis. Nevertheless, the focus must extend beyond inference as an isolated skill; rather, it is important to relate inference to the foundations of the scientific method ( Miri et al. , 2007 ) in terms of the epistemological appreciation of the functions and coordination of evidence ( Kuhn and Dean, 2004 ; Zeineddin and Abd-El-Khalick, 2010 ; Ding et al. , 2016 ) and disciplinary paradigms of truth and justification ( Moshman, 2015 ).

Although this study is limited to the domain of biology at two institutions with a relatively small number of students, the findings represent a foundational step in the direction of achieving success with more integrated learning outcomes. Hopefully, it will spur greater interest in empirically grounding discussions of the constructs of scientific reasoning and critical-thinking skills.

This study contributes to the efforts to improve science education, for both majors and nonmajors, through an empirically driven analysis of the relationships between scientific reasoning reflected in the genre of thesis writing and critical-thinking skills. This work is rooted in the usefulness of BioTAP as a method 1) to facilitate communication and learning and 2) to assess disciplinary-specific and general dimensions of science reasoning. The findings support the important role of the critical-thinking skill of inference in scientific reasoning in writing, while also highlighting ways in which other aspects of science reasoning (epistemological considerations, writing conventions, etc.) are not significantly related to critical thinking. Future research into the impact of interventions focused on specific critical-thinking skills (i.e., inference) for improved science reasoning in writing will build on this work and its implications for science education.

Supplementary Material

Acknowledgments.

We acknowledge the contributions of Kelaine Haas and Alexander Motten to the implementation and collection of data. We also thank Mine Çetinkaya-­Rundel for her insights regarding our statistical analyses. This research was funded by National Science Foundation award DUE-1525602.

• American Association for the Advancement of Science. (2011). Vision and change in undergraduate biology education: A call to action . Washington, DC: Retrieved September 26, 2017, from https://visionandchange.org/files/2013/11/aaas-VISchange-web1113.pdf . [ Google Scholar ]
• August D. (2016). California Critical Thinking Skills Test user manual and resource guide . San Jose: Insight Assessment/California Academic Press. [ Google Scholar ]
• Beyer C. H., Taylor E., Gillmore G. M. (2013). Inside the undergraduate teaching experience: The University of Washington’s growth in faculty teaching study . Albany, NY: SUNY Press. [ Google Scholar ]
• Bissell A. N., Lemons P. P. (2006). A new method for assessing critical thinking in the classroom . BioScience , ( 1 ), 66–72. https://doi.org/10.1641/0006-3568(2006)056[0066:ANMFAC]2.0.CO;2 . [ Google Scholar ]
• Blattner N. H., Frazier C. L. (2002). Developing a performance-based assessment of students’ critical thinking skills . Assessing Writing , ( 1 ), 47–64. [ Google Scholar ]
• Clase K. L., Gundlach E., Pelaez N. J. (2010). Calibrated peer review for computer-assisted learning of biological research competencies . Biochemistry and Molecular Biology Education , ( 5 ), 290–295. [ PubMed ] [ Google Scholar ]
• Condon W., Kelly-Riley D. (2004). Assessing and teaching what we value: The relationship between college-level writing and critical thinking abilities . Assessing Writing , ( 1 ), 56–75. https://doi.org/10.1016/j.asw.2004.01.003 . [ Google Scholar ]
• Ding L., Wei X., Liu X. (2016). Variations in university students’ scientific reasoning skills across majors, years, and types of institutions . Research in Science Education , ( 5 ), 613–632. https://doi.org/10.1007/s11165-015-9473-y . [ Google Scholar ]
• Dowd J. E., Connolly M. P., Thompson R. J., Jr., Reynolds J. A. (2015a). Improved reasoning in undergraduate writing through structured workshops . Journal of Economic Education , ( 1 ), 14–27. https://doi.org/10.1080/00220485.2014.978924 . [ Google Scholar ]
• Dowd J. E., Roy C. P., Thompson R. J., Jr., Reynolds J. A. (2015b). “On course” for supporting expanded participation and improving scientific reasoning in undergraduate thesis writing . Journal of Chemical Education , ( 1 ), 39–45. https://doi.org/10.1021/ed500298r . [ Google Scholar ]
• Dowd J. E., Thompson R. J., Jr., Reynolds J. A. (2016). Quantitative genre analysis of undergraduate theses: Uncovering different ways of writing and thinking in science disciplines . WAC Journal , , 36–51. [ Google Scholar ]
• Facione P. A. (1990). Critical thinking: a statement of expert consensus for purposes of educational assessment and instruction. Research findings and recommendations . Newark, DE: American Philosophical Association; Retrieved September 26, 2017, from https://philpapers.org/archive/FACCTA.pdf . [ Google Scholar ]
• Gerdeman R. D., Russell A. A., Worden K. J., Gerdeman R. D., Russell A. A., Worden K. J. (2007). Web-based student writing and reviewing in a large biology lecture course . Journal of College Science Teaching , ( 5 ), 46–52. [ Google Scholar ]
• Greenhoot A. F., Semb G., Colombo J., Schreiber T. (2004). Prior beliefs and methodological concepts in scientific reasoning . Applied Cognitive Psychology , ( 2 ), 203–221. https://doi.org/10.1002/acp.959 . [ Google Scholar ]
• Haaga D. A. F. (1993). Peer review of term papers in graduate psychology courses . Teaching of Psychology , ( 1 ), 28–32. https://doi.org/10.1207/s15328023top2001_5 . [ Google Scholar ]
• Halonen J. S., Bosack T., Clay S., McCarthy M., Dunn D. S., Hill G. W., Whitlock K. (2003). A rubric for learning, teaching, and assessing scientific inquiry in psychology . Teaching of Psychology , ( 3 ), 196–208. https://doi.org/10.1207/S15328023TOP3003_01 . [ Google Scholar ]
• Hand B., Keys C. W. (1999). Inquiry investigation . Science Teacher , ( 4 ), 27–29. [ Google Scholar ]
• Holm S. (1979). A simple sequentially rejective multiple test procedure . Scandinavian Journal of Statistics , ( 2 ), 65–70. [ Google Scholar ]
• Holyoak K. J., Morrison R. G. (2005). The Cambridge handbook of thinking and reasoning . New York: Cambridge University Press. [ Google Scholar ]
• Insight Assessment. (2016a). California Critical Thinking Skills Test (CCTST) Retrieved September 26, 2017, from www.insightassessment.com/Products/Products-Summary/Critical-Thinking-Skills-Tests/California-Critical-Thinking-Skills-Test-CCTST .
• Insight Assessment. (2016b). Sample thinking skills questions. Retrieved September 26, 2017, from www.insightassessment.com/Resources/Teaching-Training-and-Learning-Tools/node_1487 .
• Kelly G. J., Takao A. (2002). Epistemic levels in argument: An analysis of university oceanography students’ use of evidence in writing . Science Education , ( 3 ), 314–342. https://doi.org/10.1002/sce.10024 . [ Google Scholar ]
• Kuhn D., Dean D., Jr. (2004). Connecting scientific reasoning and causal inference . Journal of Cognition and Development , ( 2 ), 261–288. https://doi.org/10.1207/s15327647jcd0502_5 . [ Google Scholar ]
• Kuhn D., Iordanou K., Pease M., Wirkala C. (2008). Beyond control of variables: What needs to develop to achieve skilled scientific thinking? . Cognitive Development , ( 4 ), 435–451. https://doi.org/10.1016/j.cogdev.2008.09.006 . [ Google Scholar ]
• Lawson A. E. (2010). Basic inferences of scientific reasoning, argumentation, and discovery . Science Education , ( 2 ), 336–364. https://doi.org/­10.1002/sce.20357 . [ Google Scholar ]
• Meizlish D., LaVaque-Manty D., Silver N., Kaplan M. (2013). Think like/write like: Metacognitive strategies to foster students’ development as disciplinary thinkers and writers . In Thompson R. J. (Ed.), Changing the conversation about higher education (pp. 53–73). Lanham, MD: Rowman & Littlefield. [ Google Scholar ]
• Miri B., David B.-C., Uri Z. (2007). Purposely teaching for the promotion of higher-order thinking skills: A case of critical thinking . Research in Science Education , ( 4 ), 353–369. https://doi.org/10.1007/s11165-006-9029-2 . [ Google Scholar ]
• Moshman D. (2015). Epistemic cognition and development: The psychology of justification and truth . New York: Psychology Press. [ Google Scholar ]
• National Research Council. (2000). How people learn: Brain, mind, experience, and school . Expanded ed. Washington, DC: National Academies Press. [ Google Scholar ]
• Pukkila P. J. (2004). Introducing student inquiry in large introductory genetics classes . Genetics , ( 1 ), 11–18. https://doi.org/10.1534/genetics.166.1.11 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Quitadamo I. J., Faiola C. L., Johnson J. E., Kurtz M. J. (2008). Community-based inquiry improves critical thinking in general education biology . CBE—Life Sciences Education , ( 3 ), 327–337. https://doi.org/10.1187/cbe.07-11-0097 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Quitadamo I. J., Kurtz M. J. (2007). Learning to improve: Using writing to increase critical thinking performance in general education biology . CBE—Life Sciences Education , ( 2 ), 140–154. https://doi.org/10.1187/cbe.06-11-0203 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reynolds J. A., Smith R., Moskovitz C., Sayle A. (2009). BioTAP: A systematic approach to teaching scientific writing and evaluating undergraduate theses . BioScience , ( 10 ), 896–903. https://doi.org/10.1525/bio.2009.59.10.11 . [ Google Scholar ]
• Reynolds J. A., Thaiss C., Katkin W., Thompson R. J. (2012). Writing-to-learn in undergraduate science education: A community-based, conceptually driven approach . CBE—Life Sciences Education , ( 1 ), 17–25. https://doi.org/10.1187/cbe.11-08-0064 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reynolds J. A., Thompson R. J. (2011). Want to improve undergraduate thesis writing? Engage students and their faculty readers in scientific peer review . CBE—Life Sciences Education , ( 2 ), 209–215. https://doi.org/­10.1187/cbe.10-10-0127 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rhemtulla M., Brosseau-Liard P. E., Savalei V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions . Psychological Methods , ( 3 ), 354–373. https://doi.org/­10.1037/a0029315 . [ PubMed ] [ Google Scholar ]
• Stephenson N. S., Sadler-McKnight N. P. (2016). Developing critical thinking skills using the science writing heuristic in the chemistry laboratory . Chemistry Education Research and Practice , ( 1 ), 72–79. https://doi.org/­10.1039/C5RP00102A . [ Google Scholar ]
• Tariq V. N., Stefani L. A. J., Butcher A. C., Heylings D. J. A. (1998). Developing a new approach to the assessment of project work . Assessment and Evaluation in Higher Education , ( 3 ), 221–240. https://doi.org/­10.1080/0260293980230301 . [ Google Scholar ]
• Timmerman B. E. C., Strickland D. C., Johnson R. L., Payne J. R. (2011). Development of a “universal” rubric for assessing undergraduates’ scientific reasoning skills using scientific writing . Assessment and Evaluation in Higher Education , ( 5 ), 509–547. https://doi.org/10.1080/­02602930903540991 . [ Google Scholar ]
• Topping K. J., Smith E. F., Swanson I., Elliot A. (2000). Formative peer assessment of academic writing between postgraduate students . Assessment and Evaluation in Higher Education , ( 2 ), 149–169. https://doi.org/10.1080/713611428 . [ Google Scholar ]
• Willison J., O’Regan K. (2007). Commonly known, commonly not known, totally unknown: A framework for students becoming researchers . Higher Education Research and Development , ( 4 ), 393–409. https://doi.org/10.1080/07294360701658609 . [ Google Scholar ]
• Woodin T., Carter V. C., Fletcher L. (2010). Vision and Change in Biology Undergraduate Education: A Call for Action—Initial responses . CBE—Life Sciences Education , ( 2 ), 71–73. https://doi.org/10.1187/cbe.10-03-0044 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zeineddin A., Abd-El-Khalick F. (2010). Scientific reasoning and epistemological commitments: Coordination of theory and evidence among college science students . Journal of Research in Science Teaching , ( 9 ), 1064–1093. https://doi.org/10.1002/tea.20368 . [ Google Scholar ]
• Zimmerman C. (2000). The development of scientific reasoning skills . Developmental Review , ( 1 ), 99–149. https://doi.org/10.1006/drev.1999.0497 . [ Google Scholar ]
• Zimmerman C. (2007). The development of scientific thinking skills in elementary and middle school . Developmental Review , ( 2 ), 172–223. https://doi.org/10.1016/j.dr.2006.12.001 . [ Google Scholar ]

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Critical Thinking in Science: Fostering Scientific Reasoning Skills in Students

ALI Staff | Published  July 13, 2023

Thinking like a scientist is a central goal of all science curricula.

As students learn facts, methodologies, and methods, what matters most is that all their learning happens through the lens of scientific reasoning what matters most is that it’s all through the lens of scientific reasoning.

That way, when it comes time for them to take on a little science themselves, either in the lab or by theoretically thinking through a solution, they understand how to do it in the right context.

One component of this type of thinking is being critical. Based on facts and evidence, critical thinking in science isn’t exactly the same as critical thinking in other subjects.

Students have to doubt the information they’re given until they can prove it’s right.

They have to truly understand what’s true and what’s hearsay. It’s complex, but with the right tools and plenty of practice, students can get it right.

What is critical thinking?

This particular style of thinking stands out because it requires reflection and analysis. Based on what's logical and rational, thinking critically is all about digging deep and going beyond the surface of a question to establish the quality of the question itself.

It ensures students put their brains to work when confronted with a question rather than taking every piece of information they’re given at face value.

It’s engaged, higher-level thinking that will serve them well in school and throughout their lives.

Why is critical thinking important?

Critical thinking is important when it comes to making good decisions.

It gives us the tools to think through a choice rather than quickly picking an option — and probably guessing wrong. Think of it as the all-important ‘why.’

Why is that true? Why is that right? Why is this the only option?

Finding answers to questions like these requires critical thinking. They require you to really analyze both the question itself and the possible solutions to establish validity.

Will that choice work for me? Does this feel right based on the evidence?

How does critical thinking in science impact students?

Critical thinking is essential in science.

It’s what naturally takes students in the direction of scientific reasoning since evidence is a key component of this style of thought.

It’s not just about whether evidence is available to support a particular answer but how valid that evidence is.

It’s about whether the information the student has fits together to create a strong argument and how to use verifiable facts to get a proper response.

Critical thinking in science helps students:

• Actively evaluate information
• Identify bias
• Separate the logic within arguments
• Analyze evidence

4 Ways to promote critical thinking

Figuring out how to develop critical thinking skills in science means looking at multiple strategies and deciding what will work best at your school and in your class.

Based on your student population, their needs and abilities, not every option will be a home run.

These particular examples are all based on the idea that for students to really learn how to think critically, they have to practice doing it. 

Each focuses on engaging students with science in a way that will motivate them to work independently as they hone their scientific reasoning skills.

Project-Based Learning

Project-based learning centers on critical thinking.

Teachers can shape a project around the thinking style to give students practice with evaluating evidence or other critical thinking skills.

Critical thinking also happens during collaboration, evidence-based thought, and reflection.

For example, setting students up for a research project is not only a great way to get them to think critically, but it also helps motivate them to learn.

Allowing them to pick the topic (that isn’t easy to look up online), develop their own research questions, and establish a process to collect data to find an answer lets students personally connect to science while using critical thinking at each stage of the assignment.

They’ll have to evaluate the quality of the research they find and make evidence-based decisions.

Self-Reflection

Adding a question or two to any lab practicum or activity requiring students to pause and reflect on what they did or learned also helps them practice critical thinking.

At this point in an assignment, they’ll pause and assess independently. 

You can ask students to reflect on the conclusions they came up with for a completed activity, which really makes them think about whether there's any bias in their answer.

Addressing Assumptions

One way critical thinking aligns so perfectly with scientific reasoning is that it encourages students to challenge all assumptions. 

Evidence is king in the science classroom, but even when students work with hard facts, there comes the risk of a little assumptive thinking.

Working with students to identify assumptions in existing research or asking them to address an issue where they suspend their own judgment and simply look at established facts polishes their that critical eye.

They’re getting practice without tossing out opinions, unproven hypotheses, and speculation in exchange for real data and real results, just like a scientist has to do.

Lab Activities With Trial-And-Error

Another component of critical thinking (as well as thinking like a scientist) is figuring out what to do when you get something wrong.

Backtracking can mean you have to rethink a process, redesign an experiment, or reevaluate data because the outcomes don’t make sense, but it’s okay.

The ability to get something wrong and recover is not only a valuable life skill, but it’s where most scientific breakthroughs start. Reminding students of this is always a valuable lesson.

Labs that include comparative activities are one way to increase critical thinking skills, especially when introducing new evidence that might cause students to change their conclusions once the lab has begun.

For example, you provide students with two distinct data sets and ask them to compare them.

With only two choices, there are a finite amount of conclusions to draw, but then what happens when you bring in a third data set? Will it void certain conclusions? Will it allow students to make new conclusions, ones even more deeply rooted in evidence?

Thinking like a scientist

When students get the opportunity to think critically, they’re learning to trust the data over their ‘gut,’ to approach problems systematically and make informed decisions using ‘good’ evidence.

When practiced enough, this ability will engage students in science in a whole new way, providing them with opportunities to dig deeper and learn more.

It can help enrich science and motivate students to approach the subject just like a professional would.

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Critical Thinking

Critical thinking is a widely accepted educational goal. Its definition is contested, but the competing definitions can be understood as differing conceptions of the same basic concept: careful thinking directed to a goal. Conceptions differ with respect to the scope of such thinking, the type of goal, the criteria and norms for thinking carefully, and the thinking components on which they focus. Its adoption as an educational goal has been recommended on the basis of respect for students’ autonomy and preparing students for success in life and for democratic citizenship. “Critical thinkers” have the dispositions and abilities that lead them to think critically when appropriate. The abilities can be identified directly; the dispositions indirectly, by considering what factors contribute to or impede exercise of the abilities. Standardized tests have been developed to assess the degree to which a person possesses such dispositions and abilities. Educational intervention has been shown experimentally to improve them, particularly when it includes dialogue, anchored instruction, and mentoring. Controversies have arisen over the generalizability of critical thinking across domains, over alleged bias in critical thinking theories and instruction, and over the relationship of critical thinking to other types of thinking.

2.1 Dewey’s Three Main Examples

2.2 dewey’s other examples, 2.3 further examples, 2.4 non-examples, 3. the definition of critical thinking, 4. its value, 5. the process of thinking critically, 6. components of the process, 7. contributory dispositions and abilities, 8.1 initiating dispositions, 8.2 internal dispositions, 9. critical thinking abilities, 10. required knowledge, 11. educational methods, 12.1 the generalizability of critical thinking, 12.2 bias in critical thinking theory and pedagogy, 12.3 relationship of critical thinking to other types of thinking, other internet resources, related entries.

Use of the term ‘critical thinking’ to describe an educational goal goes back to the American philosopher John Dewey (1910), who more commonly called it ‘reflective thinking’. He defined it as

active, persistent and careful consideration of any belief or supposed form of knowledge in the light of the grounds that support it, and the further conclusions to which it tends. (Dewey 1910: 6; 1933: 9)

and identified a habit of such consideration with a scientific attitude of mind. His lengthy quotations of Francis Bacon, John Locke, and John Stuart Mill indicate that he was not the first person to propose development of a scientific attitude of mind as an educational goal.

In the 1930s, many of the schools that participated in the Eight-Year Study of the Progressive Education Association (Aikin 1942) adopted critical thinking as an educational goal, for whose achievement the study’s Evaluation Staff developed tests (Smith, Tyler, & Evaluation Staff 1942). Glaser (1941) showed experimentally that it was possible to improve the critical thinking of high school students. Bloom’s influential taxonomy of cognitive educational objectives (Bloom et al. 1956) incorporated critical thinking abilities. Ennis (1962) proposed 12 aspects of critical thinking as a basis for research on the teaching and evaluation of critical thinking ability.

Since 1980, an annual international conference in California on critical thinking and educational reform has attracted tens of thousands of educators from all levels of education and from many parts of the world. Also since 1980, the state university system in California has required all undergraduate students to take a critical thinking course. Since 1983, the Association for Informal Logic and Critical Thinking has sponsored sessions in conjunction with the divisional meetings of the American Philosophical Association (APA). In 1987, the APA’s Committee on Pre-College Philosophy commissioned a consensus statement on critical thinking for purposes of educational assessment and instruction (Facione 1990a). Researchers have developed standardized tests of critical thinking abilities and dispositions; for details, see the Supplement on Assessment . Educational jurisdictions around the world now include critical thinking in guidelines for curriculum and assessment.

For details on this history, see the Supplement on History .

2. Examples and Non-Examples

Before considering the definition of critical thinking, it will be helpful to have in mind some examples of critical thinking, as well as some examples of kinds of thinking that would apparently not count as critical thinking.

Dewey (1910: 68–71; 1933: 91–94) takes as paradigms of reflective thinking three class papers of students in which they describe their thinking. The examples range from the everyday to the scientific.

Transit : “The other day, when I was down town on 16th Street, a clock caught my eye. I saw that the hands pointed to 12:20. This suggested that I had an engagement at 124th Street, at one o’clock. I reasoned that as it had taken me an hour to come down on a surface car, I should probably be twenty minutes late if I returned the same way. I might save twenty minutes by a subway express. But was there a station near? If not, I might lose more than twenty minutes in looking for one. Then I thought of the elevated, and I saw there was such a line within two blocks. But where was the station? If it were several blocks above or below the street I was on, I should lose time instead of gaining it. My mind went back to the subway express as quicker than the elevated; furthermore, I remembered that it went nearer than the elevated to the part of 124th Street I wished to reach, so that time would be saved at the end of the journey. I concluded in favor of the subway, and reached my destination by one o’clock.” (Dewey 1910: 68–69; 1933: 91–92)

Ferryboat : “Projecting nearly horizontally from the upper deck of the ferryboat on which I daily cross the river is a long white pole, having a gilded ball at its tip. It suggested a flagpole when I first saw it; its color, shape, and gilded ball agreed with this idea, and these reasons seemed to justify me in this belief. But soon difficulties presented themselves. The pole was nearly horizontal, an unusual position for a flagpole; in the next place, there was no pulley, ring, or cord by which to attach a flag; finally, there were elsewhere on the boat two vertical staffs from which flags were occasionally flown. It seemed probable that the pole was not there for flag-flying.

“I then tried to imagine all possible purposes of the pole, and to consider for which of these it was best suited: (a) Possibly it was an ornament. But as all the ferryboats and even the tugboats carried poles, this hypothesis was rejected. (b) Possibly it was the terminal of a wireless telegraph. But the same considerations made this improbable. Besides, the more natural place for such a terminal would be the highest part of the boat, on top of the pilot house. (c) Its purpose might be to point out the direction in which the boat is moving.

“In support of this conclusion, I discovered that the pole was lower than the pilot house, so that the steersman could easily see it. Moreover, the tip was enough higher than the base, so that, from the pilot’s position, it must appear to project far out in front of the boat. Moreover, the pilot being near the front of the boat, he would need some such guide as to its direction. Tugboats would also need poles for such a purpose. This hypothesis was so much more probable than the others that I accepted it. I formed the conclusion that the pole was set up for the purpose of showing the pilot the direction in which the boat pointed, to enable him to steer correctly.” (Dewey 1910: 69–70; 1933: 92–93)

Bubbles : “In washing tumblers in hot soapsuds and placing them mouth downward on a plate, bubbles appeared on the outside of the mouth of the tumblers and then went inside. Why? The presence of bubbles suggests air, which I note must come from inside the tumbler. I see that the soapy water on the plate prevents escape of the air save as it may be caught in bubbles. But why should air leave the tumbler? There was no substance entering to force it out. It must have expanded. It expands by increase of heat, or by decrease of pressure, or both. Could the air have become heated after the tumbler was taken from the hot suds? Clearly not the air that was already entangled in the water. If heated air was the cause, cold air must have entered in transferring the tumblers from the suds to the plate. I test to see if this supposition is true by taking several more tumblers out. Some I shake so as to make sure of entrapping cold air in them. Some I take out holding mouth downward in order to prevent cold air from entering. Bubbles appear on the outside of every one of the former and on none of the latter. I must be right in my inference. Air from the outside must have been expanded by the heat of the tumbler, which explains the appearance of the bubbles on the outside. But why do they then go inside? Cold contracts. The tumbler cooled and also the air inside it. Tension was removed, and hence bubbles appeared inside. To be sure of this, I test by placing a cup of ice on the tumbler while the bubbles are still forming outside. They soon reverse” (Dewey 1910: 70–71; 1933: 93–94).

Dewey (1910, 1933) sprinkles his book with other examples of critical thinking. We will refer to the following.

Weather : A man on a walk notices that it has suddenly become cool, thinks that it is probably going to rain, looks up and sees a dark cloud obscuring the sun, and quickens his steps (1910: 6–10; 1933: 9–13).

Disorder : A man finds his rooms on his return to them in disorder with his belongings thrown about, thinks at first of burglary as an explanation, then thinks of mischievous children as being an alternative explanation, then looks to see whether valuables are missing, and discovers that they are (1910: 82–83; 1933: 166–168).

Typhoid : A physician diagnosing a patient whose conspicuous symptoms suggest typhoid avoids drawing a conclusion until more data are gathered by questioning the patient and by making tests (1910: 85–86; 1933: 170).

Blur : A moving blur catches our eye in the distance, we ask ourselves whether it is a cloud of whirling dust or a tree moving its branches or a man signaling to us, we think of other traits that should be found on each of those possibilities, and we look and see if those traits are found (1910: 102, 108; 1933: 121, 133).

Suction pump : In thinking about the suction pump, the scientist first notes that it will draw water only to a maximum height of 33 feet at sea level and to a lesser maximum height at higher elevations, selects for attention the differing atmospheric pressure at these elevations, sets up experiments in which the air is removed from a vessel containing water (when suction no longer works) and in which the weight of air at various levels is calculated, compares the results of reasoning about the height to which a given weight of air will allow a suction pump to raise water with the observed maximum height at different elevations, and finally assimilates the suction pump to such apparently different phenomena as the siphon and the rising of a balloon (1910: 150–153; 1933: 195–198).

Diamond : A passenger in a car driving in a diamond lane reserved for vehicles with at least one passenger notices that the diamond marks on the pavement are far apart in some places and close together in others. Why? The driver suggests that the reason may be that the diamond marks are not needed where there is a solid double line separating the diamond lane from the adjoining lane, but are needed when there is a dotted single line permitting crossing into the diamond lane. Further observation confirms that the diamonds are close together when a dotted line separates the diamond lane from its neighbour, but otherwise far apart.

Rash : A woman suddenly develops a very itchy red rash on her throat and upper chest. She recently noticed a mark on the back of her right hand, but was not sure whether the mark was a rash or a scrape. She lies down in bed and thinks about what might be causing the rash and what to do about it. About two weeks before, she began taking blood pressure medication that contained a sulfa drug, and the pharmacist had warned her, in view of a previous allergic reaction to a medication containing a sulfa drug, to be on the alert for an allergic reaction; however, she had been taking the medication for two weeks with no such effect. The day before, she began using a new cream on her neck and upper chest; against the new cream as the cause was mark on the back of her hand, which had not been exposed to the cream. She began taking probiotics about a month before. She also recently started new eye drops, but she supposed that manufacturers of eye drops would be careful not to include allergy-causing components in the medication. The rash might be a heat rash, since she recently was sweating profusely from her upper body. Since she is about to go away on a short vacation, where she would not have access to her usual physician, she decides to keep taking the probiotics and using the new eye drops but to discontinue the blood pressure medication and to switch back to the old cream for her neck and upper chest. She forms a plan to consult her regular physician on her return about the blood pressure medication.

Candidate : Although Dewey included no examples of thinking directed at appraising the arguments of others, such thinking has come to be considered a kind of critical thinking. We find an example of such thinking in the performance task on the Collegiate Learning Assessment (CLA+), which its sponsoring organization describes as

a performance-based assessment that provides a measure of an institution’s contribution to the development of critical-thinking and written communication skills of its students. (Council for Aid to Education 2017)

A sample task posted on its website requires the test-taker to write a report for public distribution evaluating a fictional candidate’s policy proposals and their supporting arguments, using supplied background documents, with a recommendation on whether to endorse the candidate.

Immediate acceptance of an idea that suggests itself as a solution to a problem (e.g., a possible explanation of an event or phenomenon, an action that seems likely to produce a desired result) is “uncritical thinking, the minimum of reflection” (Dewey 1910: 13). On-going suspension of judgment in the light of doubt about a possible solution is not critical thinking (Dewey 1910: 108). Critique driven by a dogmatically held political or religious ideology is not critical thinking; thus Paulo Freire (1968 [1970]) is using the term (e.g., at 1970: 71, 81, 100, 146) in a more politically freighted sense that includes not only reflection but also revolutionary action against oppression. Derivation of a conclusion from given data using an algorithm is not critical thinking.

What is critical thinking? There are many definitions. Ennis (2016) lists 14 philosophically oriented scholarly definitions and three dictionary definitions. Following Rawls (1971), who distinguished his conception of justice from a utilitarian conception but regarded them as rival conceptions of the same concept, Ennis maintains that the 17 definitions are different conceptions of the same concept. Rawls articulated the shared concept of justice as

a characteristic set of principles for assigning basic rights and duties and for determining… the proper distribution of the benefits and burdens of social cooperation. (Rawls 1971: 5)

Bailin et al. (1999b) claim that, if one considers what sorts of thinking an educator would take not to be critical thinking and what sorts to be critical thinking, one can conclude that educators typically understand critical thinking to have at least three features.

• It is done for the purpose of making up one’s mind about what to believe or do.
• The person engaging in the thinking is trying to fulfill standards of adequacy and accuracy appropriate to the thinking.
• The thinking fulfills the relevant standards to some threshold level.

One could sum up the core concept that involves these three features by saying that critical thinking is careful goal-directed thinking. This core concept seems to apply to all the examples of critical thinking described in the previous section. As for the non-examples, their exclusion depends on construing careful thinking as excluding jumping immediately to conclusions, suspending judgment no matter how strong the evidence, reasoning from an unquestioned ideological or religious perspective, and routinely using an algorithm to answer a question.

If the core of critical thinking is careful goal-directed thinking, conceptions of it can vary according to its presumed scope, its presumed goal, one’s criteria and threshold for being careful, and the thinking component on which one focuses. As to its scope, some conceptions (e.g., Dewey 1910, 1933) restrict it to constructive thinking on the basis of one’s own observations and experiments, others (e.g., Ennis 1962; Fisher & Scriven 1997; Johnson 1992) to appraisal of the products of such thinking. Ennis (1991) and Bailin et al. (1999b) take it to cover both construction and appraisal. As to its goal, some conceptions restrict it to forming a judgment (Dewey 1910, 1933; Lipman 1987; Facione 1990a). Others allow for actions as well as beliefs as the end point of a process of critical thinking (Ennis 1991; Bailin et al. 1999b). As to the criteria and threshold for being careful, definitions vary in the term used to indicate that critical thinking satisfies certain norms: “intellectually disciplined” (Scriven & Paul 1987), “reasonable” (Ennis 1991), “skillful” (Lipman 1987), “skilled” (Fisher & Scriven 1997), “careful” (Bailin & Battersby 2009). Some definitions specify these norms, referring variously to “consideration of any belief or supposed form of knowledge in the light of the grounds that support it and the further conclusions to which it tends” (Dewey 1910, 1933); “the methods of logical inquiry and reasoning” (Glaser 1941); “conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication” (Scriven & Paul 1987); the requirement that “it is sensitive to context, relies on criteria, and is self-correcting” (Lipman 1987); “evidential, conceptual, methodological, criteriological, or contextual considerations” (Facione 1990a); and “plus-minus considerations of the product in terms of appropriate standards (or criteria)” (Johnson 1992). Stanovich and Stanovich (2010) propose to ground the concept of critical thinking in the concept of rationality, which they understand as combining epistemic rationality (fitting one’s beliefs to the world) and instrumental rationality (optimizing goal fulfillment); a critical thinker, in their view, is someone with “a propensity to override suboptimal responses from the autonomous mind” (2010: 227). These variant specifications of norms for critical thinking are not necessarily incompatible with one another, and in any case presuppose the core notion of thinking carefully. As to the thinking component singled out, some definitions focus on suspension of judgment during the thinking (Dewey 1910; McPeck 1981), others on inquiry while judgment is suspended (Bailin & Battersby 2009, 2021), others on the resulting judgment (Facione 1990a), and still others on responsiveness to reasons (Siegel 1988). Kuhn (2019) takes critical thinking to be more a dialogic practice of advancing and responding to arguments than an individual ability.

In educational contexts, a definition of critical thinking is a “programmatic definition” (Scheffler 1960: 19). It expresses a practical program for achieving an educational goal. For this purpose, a one-sentence formulaic definition is much less useful than articulation of a critical thinking process, with criteria and standards for the kinds of thinking that the process may involve. The real educational goal is recognition, adoption and implementation by students of those criteria and standards. That adoption and implementation in turn consists in acquiring the knowledge, abilities and dispositions of a critical thinker.

Conceptions of critical thinking generally do not include moral integrity as part of the concept. Dewey, for example, took critical thinking to be the ultimate intellectual goal of education, but distinguished it from the development of social cooperation among school children, which he took to be the central moral goal. Ennis (1996, 2011) added to his previous list of critical thinking dispositions a group of dispositions to care about the dignity and worth of every person, which he described as a “correlative” (1996) disposition without which critical thinking would be less valuable and perhaps harmful. An educational program that aimed at developing critical thinking but not the correlative disposition to care about the dignity and worth of every person, he asserted, “would be deficient and perhaps dangerous” (Ennis 1996: 172).

Dewey thought that education for reflective thinking would be of value to both the individual and society; recognition in educational practice of the kinship to the scientific attitude of children’s native curiosity, fertile imagination and love of experimental inquiry “would make for individual happiness and the reduction of social waste” (Dewey 1910: iii). Schools participating in the Eight-Year Study took development of the habit of reflective thinking and skill in solving problems as a means to leading young people to understand, appreciate and live the democratic way of life characteristic of the United States (Aikin 1942: 17–18, 81). Harvey Siegel (1988: 55–61) has offered four considerations in support of adopting critical thinking as an educational ideal. (1) Respect for persons requires that schools and teachers honour students’ demands for reasons and explanations, deal with students honestly, and recognize the need to confront students’ independent judgment; these requirements concern the manner in which teachers treat students. (2) Education has the task of preparing children to be successful adults, a task that requires development of their self-sufficiency. (3) Education should initiate children into the rational traditions in such fields as history, science and mathematics. (4) Education should prepare children to become democratic citizens, which requires reasoned procedures and critical talents and attitudes. To supplement these considerations, Siegel (1988: 62–90) responds to two objections: the ideology objection that adoption of any educational ideal requires a prior ideological commitment and the indoctrination objection that cultivation of critical thinking cannot escape being a form of indoctrination.

Despite the diversity of our 11 examples, one can recognize a common pattern. Dewey analyzed it as consisting of five phases:

• suggestions , in which the mind leaps forward to a possible solution;
• an intellectualization of the difficulty or perplexity into a problem to be solved, a question for which the answer must be sought;
• the use of one suggestion after another as a leading idea, or hypothesis , to initiate and guide observation and other operations in collection of factual material;
• the mental elaboration of the idea or supposition as an idea or supposition ( reasoning , in the sense on which reasoning is a part, not the whole, of inference); and
• testing the hypothesis by overt or imaginative action. (Dewey 1933: 106–107; italics in original)

The process of reflective thinking consisting of these phases would be preceded by a perplexed, troubled or confused situation and followed by a cleared-up, unified, resolved situation (Dewey 1933: 106). The term ‘phases’ replaced the term ‘steps’ (Dewey 1910: 72), thus removing the earlier suggestion of an invariant sequence. Variants of the above analysis appeared in (Dewey 1916: 177) and (Dewey 1938: 101–119).

The variant formulations indicate the difficulty of giving a single logical analysis of such a varied process. The process of critical thinking may have a spiral pattern, with the problem being redefined in the light of obstacles to solving it as originally formulated. For example, the person in Transit might have concluded that getting to the appointment at the scheduled time was impossible and have reformulated the problem as that of rescheduling the appointment for a mutually convenient time. Further, defining a problem does not always follow after or lead immediately to an idea of a suggested solution. Nor should it do so, as Dewey himself recognized in describing the physician in Typhoid as avoiding any strong preference for this or that conclusion before getting further information (Dewey 1910: 85; 1933: 170). People with a hypothesis in mind, even one to which they have a very weak commitment, have a so-called “confirmation bias” (Nickerson 1998): they are likely to pay attention to evidence that confirms the hypothesis and to ignore evidence that counts against it or for some competing hypothesis. Detectives, intelligence agencies, and investigators of airplane accidents are well advised to gather relevant evidence systematically and to postpone even tentative adoption of an explanatory hypothesis until the collected evidence rules out with the appropriate degree of certainty all but one explanation. Dewey’s analysis of the critical thinking process can be faulted as well for requiring acceptance or rejection of a possible solution to a defined problem, with no allowance for deciding in the light of the available evidence to suspend judgment. Further, given the great variety of kinds of problems for which reflection is appropriate, there is likely to be variation in its component events. Perhaps the best way to conceptualize the critical thinking process is as a checklist whose component events can occur in a variety of orders, selectively, and more than once. These component events might include (1) noticing a difficulty, (2) defining the problem, (3) dividing the problem into manageable sub-problems, (4) formulating a variety of possible solutions to the problem or sub-problem, (5) determining what evidence is relevant to deciding among possible solutions to the problem or sub-problem, (6) devising a plan of systematic observation or experiment that will uncover the relevant evidence, (7) carrying out the plan of systematic observation or experimentation, (8) noting the results of the systematic observation or experiment, (9) gathering relevant testimony and information from others, (10) judging the credibility of testimony and information gathered from others, (11) drawing conclusions from gathered evidence and accepted testimony, and (12) accepting a solution that the evidence adequately supports (cf. Hitchcock 2017: 485).

Checklist conceptions of the process of critical thinking are open to the objection that they are too mechanical and procedural to fit the multi-dimensional and emotionally charged issues for which critical thinking is urgently needed (Paul 1984). For such issues, a more dialectical process is advocated, in which competing relevant world views are identified, their implications explored, and some sort of creative synthesis attempted.

If one considers the critical thinking process illustrated by the 11 examples, one can identify distinct kinds of mental acts and mental states that form part of it. To distinguish, label and briefly characterize these components is a useful preliminary to identifying abilities, skills, dispositions, attitudes, habits and the like that contribute causally to thinking critically. Identifying such abilities and habits is in turn a useful preliminary to setting educational goals. Setting the goals is in its turn a useful preliminary to designing strategies for helping learners to achieve the goals and to designing ways of measuring the extent to which learners have done so. Such measures provide both feedback to learners on their achievement and a basis for experimental research on the effectiveness of various strategies for educating people to think critically. Let us begin, then, by distinguishing the kinds of mental acts and mental events that can occur in a critical thinking process.

• Observing : One notices something in one’s immediate environment (sudden cooling of temperature in Weather , bubbles forming outside a glass and then going inside in Bubbles , a moving blur in the distance in Blur , a rash in Rash ). Or one notes the results of an experiment or systematic observation (valuables missing in Disorder , no suction without air pressure in Suction pump )
• Feeling : One feels puzzled or uncertain about something (how to get to an appointment on time in Transit , why the diamonds vary in spacing in Diamond ). One wants to resolve this perplexity. One feels satisfaction once one has worked out an answer (to take the subway express in Transit , diamonds closer when needed as a warning in Diamond ).
• Wondering : One formulates a question to be addressed (why bubbles form outside a tumbler taken from hot water in Bubbles , how suction pumps work in Suction pump , what caused the rash in Rash ).
• Imagining : One thinks of possible answers (bus or subway or elevated in Transit , flagpole or ornament or wireless communication aid or direction indicator in Ferryboat , allergic reaction or heat rash in Rash ).
• Inferring : One works out what would be the case if a possible answer were assumed (valuables missing if there has been a burglary in Disorder , earlier start to the rash if it is an allergic reaction to a sulfa drug in Rash ). Or one draws a conclusion once sufficient relevant evidence is gathered (take the subway in Transit , burglary in Disorder , discontinue blood pressure medication and new cream in Rash ).
• Knowledge : One uses stored knowledge of the subject-matter to generate possible answers or to infer what would be expected on the assumption of a particular answer (knowledge of a city’s public transit system in Transit , of the requirements for a flagpole in Ferryboat , of Boyle’s law in Bubbles , of allergic reactions in Rash ).
• Experimenting : One designs and carries out an experiment or a systematic observation to find out whether the results deduced from a possible answer will occur (looking at the location of the flagpole in relation to the pilot’s position in Ferryboat , putting an ice cube on top of a tumbler taken from hot water in Bubbles , measuring the height to which a suction pump will draw water at different elevations in Suction pump , noticing the spacing of diamonds when movement to or from a diamond lane is allowed in Diamond ).
• Consulting : One finds a source of information, gets the information from the source, and makes a judgment on whether to accept it. None of our 11 examples include searching for sources of information. In this respect they are unrepresentative, since most people nowadays have almost instant access to information relevant to answering any question, including many of those illustrated by the examples. However, Candidate includes the activities of extracting information from sources and evaluating its credibility.
• Identifying and analyzing arguments : One notices an argument and works out its structure and content as a preliminary to evaluating its strength. This activity is central to Candidate . It is an important part of a critical thinking process in which one surveys arguments for various positions on an issue.
• Judging : One makes a judgment on the basis of accumulated evidence and reasoning, such as the judgment in Ferryboat that the purpose of the pole is to provide direction to the pilot.
• Deciding : One makes a decision on what to do or on what policy to adopt, as in the decision in Transit to take the subway.

By definition, a person who does something voluntarily is both willing and able to do that thing at that time. Both the willingness and the ability contribute causally to the person’s action, in the sense that the voluntary action would not occur if either (or both) of these were lacking. For example, suppose that one is standing with one’s arms at one’s sides and one voluntarily lifts one’s right arm to an extended horizontal position. One would not do so if one were unable to lift one’s arm, if for example one’s right side was paralyzed as the result of a stroke. Nor would one do so if one were unwilling to lift one’s arm, if for example one were participating in a street demonstration at which a white supremacist was urging the crowd to lift their right arm in a Nazi salute and one were unwilling to express support in this way for the racist Nazi ideology. The same analysis applies to a voluntary mental process of thinking critically. It requires both willingness and ability to think critically, including willingness and ability to perform each of the mental acts that compose the process and to coordinate those acts in a sequence that is directed at resolving the initiating perplexity.

Consider willingness first. We can identify causal contributors to willingness to think critically by considering factors that would cause a person who was able to think critically about an issue nevertheless not to do so (Hamby 2014). For each factor, the opposite condition thus contributes causally to willingness to think critically on a particular occasion. For example, people who habitually jump to conclusions without considering alternatives will not think critically about issues that arise, even if they have the required abilities. The contrary condition of willingness to suspend judgment is thus a causal contributor to thinking critically.

Now consider ability. In contrast to the ability to move one’s arm, which can be completely absent because a stroke has left the arm paralyzed, the ability to think critically is a developed ability, whose absence is not a complete absence of ability to think but absence of ability to think well. We can identify the ability to think well directly, in terms of the norms and standards for good thinking. In general, to be able do well the thinking activities that can be components of a critical thinking process, one needs to know the concepts and principles that characterize their good performance, to recognize in particular cases that the concepts and principles apply, and to apply them. The knowledge, recognition and application may be procedural rather than declarative. It may be domain-specific rather than widely applicable, and in either case may need subject-matter knowledge, sometimes of a deep kind.

Reflections of the sort illustrated by the previous two paragraphs have led scholars to identify the knowledge, abilities and dispositions of a “critical thinker”, i.e., someone who thinks critically whenever it is appropriate to do so. We turn now to these three types of causal contributors to thinking critically. We start with dispositions, since arguably these are the most powerful contributors to being a critical thinker, can be fostered at an early stage of a child’s development, and are susceptible to general improvement (Glaser 1941: 175)

8. Critical Thinking Dispositions

Educational researchers use the term ‘dispositions’ broadly for the habits of mind and attitudes that contribute causally to being a critical thinker. Some writers (e.g., Paul & Elder 2006; Hamby 2014; Bailin & Battersby 2016a) propose to use the term ‘virtues’ for this dimension of a critical thinker. The virtues in question, although they are virtues of character, concern the person’s ways of thinking rather than the person’s ways of behaving towards others. They are not moral virtues but intellectual virtues, of the sort articulated by Zagzebski (1996) and discussed by Turri, Alfano, and Greco (2017).

On a realistic conception, thinking dispositions or intellectual virtues are real properties of thinkers. They are general tendencies, propensities, or inclinations to think in particular ways in particular circumstances, and can be genuinely explanatory (Siegel 1999). Sceptics argue that there is no evidence for a specific mental basis for the habits of mind that contribute to thinking critically, and that it is pedagogically misleading to posit such a basis (Bailin et al. 1999a). Whatever their status, critical thinking dispositions need motivation for their initial formation in a child—motivation that may be external or internal. As children develop, the force of habit will gradually become important in sustaining the disposition (Nieto & Valenzuela 2012). Mere force of habit, however, is unlikely to sustain critical thinking dispositions. Critical thinkers must value and enjoy using their knowledge and abilities to think things through for themselves. They must be committed to, and lovers of, inquiry.

A person may have a critical thinking disposition with respect to only some kinds of issues. For example, one could be open-minded about scientific issues but not about religious issues. Similarly, one could be confident in one’s ability to reason about the theological implications of the existence of evil in the world but not in one’s ability to reason about the best design for a guided ballistic missile.

Facione (1990a: 25) divides “affective dispositions” of critical thinking into approaches to life and living in general and approaches to specific issues, questions or problems. Adapting this distinction, one can usefully divide critical thinking dispositions into initiating dispositions (those that contribute causally to starting to think critically about an issue) and internal dispositions (those that contribute causally to doing a good job of thinking critically once one has started). The two categories are not mutually exclusive. For example, open-mindedness, in the sense of willingness to consider alternative points of view to one’s own, is both an initiating and an internal disposition.

Using the strategy of considering factors that would block people with the ability to think critically from doing so, we can identify as initiating dispositions for thinking critically attentiveness, a habit of inquiry, self-confidence, courage, open-mindedness, willingness to suspend judgment, trust in reason, wanting evidence for one’s beliefs, and seeking the truth. We consider briefly what each of these dispositions amounts to, in each case citing sources that acknowledge them.

• Attentiveness : One will not think critically if one fails to recognize an issue that needs to be thought through. For example, the pedestrian in Weather would not have looked up if he had not noticed that the air was suddenly cooler. To be a critical thinker, then, one needs to be habitually attentive to one’s surroundings, noticing not only what one senses but also sources of perplexity in messages received and in one’s own beliefs and attitudes (Facione 1990a: 25; Facione, Facione, & Giancarlo 2001).
• Habit of inquiry : Inquiry is effortful, and one needs an internal push to engage in it. For example, the student in Bubbles could easily have stopped at idle wondering about the cause of the bubbles rather than reasoning to a hypothesis, then designing and executing an experiment to test it. Thus willingness to think critically needs mental energy and initiative. What can supply that energy? Love of inquiry, or perhaps just a habit of inquiry. Hamby (2015) has argued that willingness to inquire is the central critical thinking virtue, one that encompasses all the others. It is recognized as a critical thinking disposition by Dewey (1910: 29; 1933: 35), Glaser (1941: 5), Ennis (1987: 12; 1991: 8), Facione (1990a: 25), Bailin et al. (1999b: 294), Halpern (1998: 452), and Facione, Facione, & Giancarlo (2001).
• Self-confidence : Lack of confidence in one’s abilities can block critical thinking. For example, if the woman in Rash lacked confidence in her ability to figure things out for herself, she might just have assumed that the rash on her chest was the allergic reaction to her medication against which the pharmacist had warned her. Thus willingness to think critically requires confidence in one’s ability to inquire (Facione 1990a: 25; Facione, Facione, & Giancarlo 2001).
• Courage : Fear of thinking for oneself can stop one from doing it. Thus willingness to think critically requires intellectual courage (Paul & Elder 2006: 16).
• Open-mindedness : A dogmatic attitude will impede thinking critically. For example, a person who adheres rigidly to a “pro-choice” position on the issue of the legal status of induced abortion is likely to be unwilling to consider seriously the issue of when in its development an unborn child acquires a moral right to life. Thus willingness to think critically requires open-mindedness, in the sense of a willingness to examine questions to which one already accepts an answer but which further evidence or reasoning might cause one to answer differently (Dewey 1933; Facione 1990a; Ennis 1991; Bailin et al. 1999b; Halpern 1998, Facione, Facione, & Giancarlo 2001). Paul (1981) emphasizes open-mindedness about alternative world-views, and recommends a dialectical approach to integrating such views as central to what he calls “strong sense” critical thinking. In three studies, Haran, Ritov, & Mellers (2013) found that actively open-minded thinking, including “the tendency to weigh new evidence against a favored belief, to spend sufficient time on a problem before giving up, and to consider carefully the opinions of others in forming one’s own”, led study participants to acquire information and thus to make accurate estimations.
• Willingness to suspend judgment : Premature closure on an initial solution will block critical thinking. Thus willingness to think critically requires a willingness to suspend judgment while alternatives are explored (Facione 1990a; Ennis 1991; Halpern 1998).
• Trust in reason : Since distrust in the processes of reasoned inquiry will dissuade one from engaging in it, trust in them is an initiating critical thinking disposition (Facione 1990a, 25; Bailin et al. 1999b: 294; Facione, Facione, & Giancarlo 2001; Paul & Elder 2006). In reaction to an allegedly exclusive emphasis on reason in critical thinking theory and pedagogy, Thayer-Bacon (2000) argues that intuition, imagination, and emotion have important roles to play in an adequate conception of critical thinking that she calls “constructive thinking”. From her point of view, critical thinking requires trust not only in reason but also in intuition, imagination, and emotion.
• Seeking the truth : If one does not care about the truth but is content to stick with one’s initial bias on an issue, then one will not think critically about it. Seeking the truth is thus an initiating critical thinking disposition (Bailin et al. 1999b: 294; Facione, Facione, & Giancarlo 2001). A disposition to seek the truth is implicit in more specific critical thinking dispositions, such as trying to be well-informed, considering seriously points of view other than one’s own, looking for alternatives, suspending judgment when the evidence is insufficient, and adopting a position when the evidence supporting it is sufficient.

Some of the initiating dispositions, such as open-mindedness and willingness to suspend judgment, are also internal critical thinking dispositions, in the sense of mental habits or attitudes that contribute causally to doing a good job of critical thinking once one starts the process. But there are many other internal critical thinking dispositions. Some of them are parasitic on one’s conception of good thinking. For example, it is constitutive of good thinking about an issue to formulate the issue clearly and to maintain focus on it. For this purpose, one needs not only the corresponding ability but also the corresponding disposition. Ennis (1991: 8) describes it as the disposition “to determine and maintain focus on the conclusion or question”, Facione (1990a: 25) as “clarity in stating the question or concern”. Other internal dispositions are motivators to continue or adjust the critical thinking process, such as willingness to persist in a complex task and willingness to abandon nonproductive strategies in an attempt to self-correct (Halpern 1998: 452). For a list of identified internal critical thinking dispositions, see the Supplement on Internal Critical Thinking Dispositions .

Some theorists postulate skills, i.e., acquired abilities, as operative in critical thinking. It is not obvious, however, that a good mental act is the exercise of a generic acquired skill. Inferring an expected time of arrival, as in Transit , has some generic components but also uses non-generic subject-matter knowledge. Bailin et al. (1999a) argue against viewing critical thinking skills as generic and discrete, on the ground that skilled performance at a critical thinking task cannot be separated from knowledge of concepts and from domain-specific principles of good thinking. Talk of skills, they concede, is unproblematic if it means merely that a person with critical thinking skills is capable of intelligent performance.

Despite such scepticism, theorists of critical thinking have listed as general contributors to critical thinking what they variously call abilities (Glaser 1941; Ennis 1962, 1991), skills (Facione 1990a; Halpern 1998) or competencies (Fisher & Scriven 1997). Amalgamating these lists would produce a confusing and chaotic cornucopia of more than 50 possible educational objectives, with only partial overlap among them. It makes sense instead to try to understand the reasons for the multiplicity and diversity, and to make a selection according to one’s own reasons for singling out abilities to be developed in a critical thinking curriculum. Two reasons for diversity among lists of critical thinking abilities are the underlying conception of critical thinking and the envisaged educational level. Appraisal-only conceptions, for example, involve a different suite of abilities than constructive-only conceptions. Some lists, such as those in (Glaser 1941), are put forward as educational objectives for secondary school students, whereas others are proposed as objectives for college students (e.g., Facione 1990a).

The abilities described in the remaining paragraphs of this section emerge from reflection on the general abilities needed to do well the thinking activities identified in section 6 as components of the critical thinking process described in section 5 . The derivation of each collection of abilities is accompanied by citation of sources that list such abilities and of standardized tests that claim to test them.

Observational abilities : Careful and accurate observation sometimes requires specialist expertise and practice, as in the case of observing birds and observing accident scenes. However, there are general abilities of noticing what one’s senses are picking up from one’s environment and of being able to articulate clearly and accurately to oneself and others what one has observed. It helps in exercising them to be able to recognize and take into account factors that make one’s observation less trustworthy, such as prior framing of the situation, inadequate time, deficient senses, poor observation conditions, and the like. It helps as well to be skilled at taking steps to make one’s observation more trustworthy, such as moving closer to get a better look, measuring something three times and taking the average, and checking what one thinks one is observing with someone else who is in a good position to observe it. It also helps to be skilled at recognizing respects in which one’s report of one’s observation involves inference rather than direct observation, so that one can then consider whether the inference is justified. These abilities come into play as well when one thinks about whether and with what degree of confidence to accept an observation report, for example in the study of history or in a criminal investigation or in assessing news reports. Observational abilities show up in some lists of critical thinking abilities (Ennis 1962: 90; Facione 1990a: 16; Ennis 1991: 9). There are items testing a person’s ability to judge the credibility of observation reports in the Cornell Critical Thinking Tests, Levels X and Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005). Norris and King (1983, 1985, 1990a, 1990b) is a test of ability to appraise observation reports.

Emotional abilities : The emotions that drive a critical thinking process are perplexity or puzzlement, a wish to resolve it, and satisfaction at achieving the desired resolution. Children experience these emotions at an early age, without being trained to do so. Education that takes critical thinking as a goal needs only to channel these emotions and to make sure not to stifle them. Collaborative critical thinking benefits from ability to recognize one’s own and others’ emotional commitments and reactions.

Questioning abilities : A critical thinking process needs transformation of an inchoate sense of perplexity into a clear question. Formulating a question well requires not building in questionable assumptions, not prejudging the issue, and using language that in context is unambiguous and precise enough (Ennis 1962: 97; 1991: 9).

Imaginative abilities : Thinking directed at finding the correct causal explanation of a general phenomenon or particular event requires an ability to imagine possible explanations. Thinking about what policy or plan of action to adopt requires generation of options and consideration of possible consequences of each option. Domain knowledge is required for such creative activity, but a general ability to imagine alternatives is helpful and can be nurtured so as to become easier, quicker, more extensive, and deeper (Dewey 1910: 34–39; 1933: 40–47). Facione (1990a) and Halpern (1998) include the ability to imagine alternatives as a critical thinking ability.

Inferential abilities : The ability to draw conclusions from given information, and to recognize with what degree of certainty one’s own or others’ conclusions follow, is universally recognized as a general critical thinking ability. All 11 examples in section 2 of this article include inferences, some from hypotheses or options (as in Transit , Ferryboat and Disorder ), others from something observed (as in Weather and Rash ). None of these inferences is formally valid. Rather, they are licensed by general, sometimes qualified substantive rules of inference (Toulmin 1958) that rest on domain knowledge—that a bus trip takes about the same time in each direction, that the terminal of a wireless telegraph would be located on the highest possible place, that sudden cooling is often followed by rain, that an allergic reaction to a sulfa drug generally shows up soon after one starts taking it. It is a matter of controversy to what extent the specialized ability to deduce conclusions from premisses using formal rules of inference is needed for critical thinking. Dewey (1933) locates logical forms in setting out the products of reflection rather than in the process of reflection. Ennis (1981a), on the other hand, maintains that a liberally-educated person should have the following abilities: to translate natural-language statements into statements using the standard logical operators, to use appropriately the language of necessary and sufficient conditions, to deal with argument forms and arguments containing symbols, to determine whether in virtue of an argument’s form its conclusion follows necessarily from its premisses, to reason with logically complex propositions, and to apply the rules and procedures of deductive logic. Inferential abilities are recognized as critical thinking abilities by Glaser (1941: 6), Facione (1990a: 9), Ennis (1991: 9), Fisher & Scriven (1997: 99, 111), and Halpern (1998: 452). Items testing inferential abilities constitute two of the five subtests of the Watson Glaser Critical Thinking Appraisal (Watson & Glaser 1980a, 1980b, 1994), two of the four sections in the Cornell Critical Thinking Test Level X (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005), three of the seven sections in the Cornell Critical Thinking Test Level Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005), 11 of the 34 items on Forms A and B of the California Critical Thinking Skills Test (Facione 1990b, 1992), and a high but variable proportion of the 25 selected-response questions in the Collegiate Learning Assessment (Council for Aid to Education 2017).

Experimenting abilities : Knowing how to design and execute an experiment is important not just in scientific research but also in everyday life, as in Rash . Dewey devoted a whole chapter of his How We Think (1910: 145–156; 1933: 190–202) to the superiority of experimentation over observation in advancing knowledge. Experimenting abilities come into play at one remove in appraising reports of scientific studies. Skill in designing and executing experiments includes the acknowledged abilities to appraise evidence (Glaser 1941: 6), to carry out experiments and to apply appropriate statistical inference techniques (Facione 1990a: 9), to judge inductions to an explanatory hypothesis (Ennis 1991: 9), and to recognize the need for an adequately large sample size (Halpern 1998). The Cornell Critical Thinking Test Level Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005) includes four items (out of 52) on experimental design. The Collegiate Learning Assessment (Council for Aid to Education 2017) makes room for appraisal of study design in both its performance task and its selected-response questions.

Consulting abilities : Skill at consulting sources of information comes into play when one seeks information to help resolve a problem, as in Candidate . Ability to find and appraise information includes ability to gather and marshal pertinent information (Glaser 1941: 6), to judge whether a statement made by an alleged authority is acceptable (Ennis 1962: 84), to plan a search for desired information (Facione 1990a: 9), and to judge the credibility of a source (Ennis 1991: 9). Ability to judge the credibility of statements is tested by 24 items (out of 76) in the Cornell Critical Thinking Test Level X (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005) and by four items (out of 52) in the Cornell Critical Thinking Test Level Z (Ennis & Millman 1971; Ennis, Millman, & Tomko 1985, 2005). The College Learning Assessment’s performance task requires evaluation of whether information in documents is credible or unreliable (Council for Aid to Education 2017).

Argument analysis abilities : The ability to identify and analyze arguments contributes to the process of surveying arguments on an issue in order to form one’s own reasoned judgment, as in Candidate . The ability to detect and analyze arguments is recognized as a critical thinking skill by Facione (1990a: 7–8), Ennis (1991: 9) and Halpern (1998). Five items (out of 34) on the California Critical Thinking Skills Test (Facione 1990b, 1992) test skill at argument analysis. The College Learning Assessment (Council for Aid to Education 2017) incorporates argument analysis in its selected-response tests of critical reading and evaluation and of critiquing an argument.

Judging skills and deciding skills : Skill at judging and deciding is skill at recognizing what judgment or decision the available evidence and argument supports, and with what degree of confidence. It is thus a component of the inferential skills already discussed.

Lists and tests of critical thinking abilities often include two more abilities: identifying assumptions and constructing and evaluating definitions.

In addition to dispositions and abilities, critical thinking needs knowledge: of critical thinking concepts, of critical thinking principles, and of the subject-matter of the thinking.

We can derive a short list of concepts whose understanding contributes to critical thinking from the critical thinking abilities described in the preceding section. Observational abilities require an understanding of the difference between observation and inference. Questioning abilities require an understanding of the concepts of ambiguity and vagueness. Inferential abilities require an understanding of the difference between conclusive and defeasible inference (traditionally, between deduction and induction), as well as of the difference between necessary and sufficient conditions. Experimenting abilities require an understanding of the concepts of hypothesis, null hypothesis, assumption and prediction, as well as of the concept of statistical significance and of its difference from importance. They also require an understanding of the difference between an experiment and an observational study, and in particular of the difference between a randomized controlled trial, a prospective correlational study and a retrospective (case-control) study. Argument analysis abilities require an understanding of the concepts of argument, premiss, assumption, conclusion and counter-consideration. Additional critical thinking concepts are proposed by Bailin et al. (1999b: 293), Fisher & Scriven (1997: 105–106), Black (2012), and Blair (2021).

According to Glaser (1941: 25), ability to think critically requires knowledge of the methods of logical inquiry and reasoning. If we review the list of abilities in the preceding section, however, we can see that some of them can be acquired and exercised merely through practice, possibly guided in an educational setting, followed by feedback. Searching intelligently for a causal explanation of some phenomenon or event requires that one consider a full range of possible causal contributors, but it seems more important that one implements this principle in one’s practice than that one is able to articulate it. What is important is “operational knowledge” of the standards and principles of good thinking (Bailin et al. 1999b: 291–293). But the development of such critical thinking abilities as designing an experiment or constructing an operational definition can benefit from learning their underlying theory. Further, explicit knowledge of quirks of human thinking seems useful as a cautionary guide. Human memory is not just fallible about details, as people learn from their own experiences of misremembering, but is so malleable that a detailed, clear and vivid recollection of an event can be a total fabrication (Loftus 2017). People seek or interpret evidence in ways that are partial to their existing beliefs and expectations, often unconscious of their “confirmation bias” (Nickerson 1998). Not only are people subject to this and other cognitive biases (Kahneman 2011), of which they are typically unaware, but it may be counter-productive for one to make oneself aware of them and try consciously to counteract them or to counteract social biases such as racial or sexual stereotypes (Kenyon & Beaulac 2014). It is helpful to be aware of these facts and of the superior effectiveness of blocking the operation of biases—for example, by making an immediate record of one’s observations, refraining from forming a preliminary explanatory hypothesis, blind refereeing, double-blind randomized trials, and blind grading of students’ work. It is also helpful to be aware of the prevalence of “noise” (unwanted unsystematic variability of judgments), of how to detect noise (through a noise audit), and of how to reduce noise: make accuracy the goal, think statistically, break a process of arriving at a judgment into independent tasks, resist premature intuitions, in a group get independent judgments first, favour comparative judgments and scales (Kahneman, Sibony, & Sunstein 2021). It is helpful as well to be aware of the concept of “bounded rationality” in decision-making and of the related distinction between “satisficing” and optimizing (Simon 1956; Gigerenzer 2001).

Critical thinking about an issue requires substantive knowledge of the domain to which the issue belongs. Critical thinking abilities are not a magic elixir that can be applied to any issue whatever by somebody who has no knowledge of the facts relevant to exploring that issue. For example, the student in Bubbles needed to know that gases do not penetrate solid objects like a glass, that air expands when heated, that the volume of an enclosed gas varies directly with its temperature and inversely with its pressure, and that hot objects will spontaneously cool down to the ambient temperature of their surroundings unless kept hot by insulation or a source of heat. Critical thinkers thus need a rich fund of subject-matter knowledge relevant to the variety of situations they encounter. This fact is recognized in the inclusion among critical thinking dispositions of a concern to become and remain generally well informed.

Experimental educational interventions, with control groups, have shown that education can improve critical thinking skills and dispositions, as measured by standardized tests. For information about these tests, see the Supplement on Assessment .

What educational methods are most effective at developing the dispositions, abilities and knowledge of a critical thinker? In a comprehensive meta-analysis of experimental and quasi-experimental studies of strategies for teaching students to think critically, Abrami et al. (2015) found that dialogue, anchored instruction, and mentoring each increased the effectiveness of the educational intervention, and that they were most effective when combined. They also found that in these studies a combination of separate instruction in critical thinking with subject-matter instruction in which students are encouraged to think critically was more effective than either by itself. However, the difference was not statistically significant; that is, it might have arisen by chance.

Most of these studies lack the longitudinal follow-up required to determine whether the observed differential improvements in critical thinking abilities or dispositions continue over time, for example until high school or college graduation. For details on studies of methods of developing critical thinking skills and dispositions, see the Supplement on Educational Methods .

12. Controversies

Scholars have denied the generalizability of critical thinking abilities across subject domains, have alleged bias in critical thinking theory and pedagogy, and have investigated the relationship of critical thinking to other kinds of thinking.

McPeck (1981) attacked the thinking skills movement of the 1970s, including the critical thinking movement. He argued that there are no general thinking skills, since thinking is always thinking about some subject-matter. It is futile, he claimed, for schools and colleges to teach thinking as if it were a separate subject. Rather, teachers should lead their pupils to become autonomous thinkers by teaching school subjects in a way that brings out their cognitive structure and that encourages and rewards discussion and argument. As some of his critics (e.g., Paul 1985; Siegel 1985) pointed out, McPeck’s central argument needs elaboration, since it has obvious counter-examples in writing and speaking, for which (up to a certain level of complexity) there are teachable general abilities even though they are always about some subject-matter. To make his argument convincing, McPeck needs to explain how thinking differs from writing and speaking in a way that does not permit useful abstraction of its components from the subject-matters with which it deals. He has not done so. Nevertheless, his position that the dispositions and abilities of a critical thinker are best developed in the context of subject-matter instruction is shared by many theorists of critical thinking, including Dewey (1910, 1933), Glaser (1941), Passmore (1980), Weinstein (1990), Bailin et al. (1999b), and Willingham (2019).

McPeck’s challenge prompted reflection on the extent to which critical thinking is subject-specific. McPeck argued for a strong subject-specificity thesis, according to which it is a conceptual truth that all critical thinking abilities are specific to a subject. (He did not however extend his subject-specificity thesis to critical thinking dispositions. In particular, he took the disposition to suspend judgment in situations of cognitive dissonance to be a general disposition.) Conceptual subject-specificity is subject to obvious counter-examples, such as the general ability to recognize confusion of necessary and sufficient conditions. A more modest thesis, also endorsed by McPeck, is epistemological subject-specificity, according to which the norms of good thinking vary from one field to another. Epistemological subject-specificity clearly holds to a certain extent; for example, the principles in accordance with which one solves a differential equation are quite different from the principles in accordance with which one determines whether a painting is a genuine Picasso. But the thesis suffers, as Ennis (1989) points out, from vagueness of the concept of a field or subject and from the obvious existence of inter-field principles, however broadly the concept of a field is construed. For example, the principles of hypothetico-deductive reasoning hold for all the varied fields in which such reasoning occurs. A third kind of subject-specificity is empirical subject-specificity, according to which as a matter of empirically observable fact a person with the abilities and dispositions of a critical thinker in one area of investigation will not necessarily have them in another area of investigation.

The thesis of empirical subject-specificity raises the general problem of transfer. If critical thinking abilities and dispositions have to be developed independently in each school subject, how are they of any use in dealing with the problems of everyday life and the political and social issues of contemporary society, most of which do not fit into the framework of a traditional school subject? Proponents of empirical subject-specificity tend to argue that transfer is more likely to occur if there is critical thinking instruction in a variety of domains, with explicit attention to dispositions and abilities that cut across domains. But evidence for this claim is scanty. There is a need for well-designed empirical studies that investigate the conditions that make transfer more likely.

It is common ground in debates about the generality or subject-specificity of critical thinking dispositions and abilities that critical thinking about any topic requires background knowledge about the topic. For example, the most sophisticated understanding of the principles of hypothetico-deductive reasoning is of no help unless accompanied by some knowledge of what might be plausible explanations of some phenomenon under investigation.

Critics have objected to bias in the theory, pedagogy and practice of critical thinking. Commentators (e.g., Alston 1995; Ennis 1998) have noted that anyone who takes a position has a bias in the neutral sense of being inclined in one direction rather than others. The critics, however, are objecting to bias in the pejorative sense of an unjustified favoring of certain ways of knowing over others, frequently alleging that the unjustly favoured ways are those of a dominant sex or culture (Bailin 1995). These ways favour:

• reinforcement of egocentric and sociocentric biases over dialectical engagement with opposing world-views (Paul 1981, 1984; Warren 1998)
• distancing from the object of inquiry over closeness to it (Martin 1992; Thayer-Bacon 1992)
• indifference to the situation of others over care for them (Martin 1992)
• orientation to thought over orientation to action (Martin 1992)
• being reasonable over caring to understand people’s ideas (Thayer-Bacon 1993)
• being neutral and objective over being embodied and situated (Thayer-Bacon 1995a)
• doubting over believing (Thayer-Bacon 1995b)
• reason over emotion, imagination and intuition (Thayer-Bacon 2000)
• solitary thinking over collaborative thinking (Thayer-Bacon 2000)
• written and spoken assignments over other forms of expression (Alston 2001)
• attention to written and spoken communications over attention to human problems (Alston 2001)
• winning debates in the public sphere over making and understanding meaning (Alston 2001)

A common thread in this smorgasbord of accusations is dissatisfaction with focusing on the logical analysis and evaluation of reasoning and arguments. While these authors acknowledge that such analysis and evaluation is part of critical thinking and should be part of its conceptualization and pedagogy, they insist that it is only a part. Paul (1981), for example, bemoans the tendency of atomistic teaching of methods of analyzing and evaluating arguments to turn students into more able sophists, adept at finding fault with positions and arguments with which they disagree but even more entrenched in the egocentric and sociocentric biases with which they began. Martin (1992) and Thayer-Bacon (1992) cite with approval the self-reported intimacy with their subject-matter of leading researchers in biology and medicine, an intimacy that conflicts with the distancing allegedly recommended in standard conceptions and pedagogy of critical thinking. Thayer-Bacon (2000) contrasts the embodied and socially embedded learning of her elementary school students in a Montessori school, who used their imagination, intuition and emotions as well as their reason, with conceptions of critical thinking as

thinking that is used to critique arguments, offer justifications, and make judgments about what are the good reasons, or the right answers. (Thayer-Bacon 2000: 127–128)

Alston (2001) reports that her students in a women’s studies class were able to see the flaws in the Cinderella myth that pervades much romantic fiction but in their own romantic relationships still acted as if all failures were the woman’s fault and still accepted the notions of love at first sight and living happily ever after. Students, she writes, should

be able to connect their intellectual critique to a more affective, somatic, and ethical account of making risky choices that have sexist, racist, classist, familial, sexual, or other consequences for themselves and those both near and far… critical thinking that reads arguments, texts, or practices merely on the surface without connections to feeling/desiring/doing or action lacks an ethical depth that should infuse the difference between mere cognitive activity and something we want to call critical thinking. (Alston 2001: 34)

Some critics portray such biases as unfair to women. Thayer-Bacon (1992), for example, has charged modern critical thinking theory with being sexist, on the ground that it separates the self from the object and causes one to lose touch with one’s inner voice, and thus stigmatizes women, who (she asserts) link self to object and listen to their inner voice. Her charge does not imply that women as a group are on average less able than men to analyze and evaluate arguments. Facione (1990c) found no difference by sex in performance on his California Critical Thinking Skills Test. Kuhn (1991: 280–281) found no difference by sex in either the disposition or the competence to engage in argumentative thinking.

The critics propose a variety of remedies for the biases that they allege. In general, they do not propose to eliminate or downplay critical thinking as an educational goal. Rather, they propose to conceptualize critical thinking differently and to change its pedagogy accordingly. Their pedagogical proposals arise logically from their objections. They can be summarized as follows:

• Focus on argument networks with dialectical exchanges reflecting contesting points of view rather than on atomic arguments, so as to develop “strong sense” critical thinking that transcends egocentric and sociocentric biases (Paul 1981, 1984).
• Foster closeness to the subject-matter and feeling connected to others in order to inform a humane democracy (Martin 1992).
• Develop “constructive thinking” as a social activity in a community of physically embodied and socially embedded inquirers with personal voices who value not only reason but also imagination, intuition and emotion (Thayer-Bacon 2000).
• In developing critical thinking in school subjects, treat as important neither skills nor dispositions but opening worlds of meaning (Alston 2001).
• Attend to the development of critical thinking dispositions as well as skills, and adopt the “critical pedagogy” practised and advocated by Freire (1968 [1970]) and hooks (1994) (Dalgleish, Girard, & Davies 2017).

A common thread in these proposals is treatment of critical thinking as a social, interactive, personally engaged activity like that of a quilting bee or a barn-raising (Thayer-Bacon 2000) rather than as an individual, solitary, distanced activity symbolized by Rodin’s The Thinker . One can get a vivid description of education with the former type of goal from the writings of bell hooks (1994, 2010). Critical thinking for her is open-minded dialectical exchange across opposing standpoints and from multiple perspectives, a conception similar to Paul’s “strong sense” critical thinking (Paul 1981). She abandons the structure of domination in the traditional classroom. In an introductory course on black women writers, for example, she assigns students to write an autobiographical paragraph about an early racial memory, then to read it aloud as the others listen, thus affirming the uniqueness and value of each voice and creating a communal awareness of the diversity of the group’s experiences (hooks 1994: 84). Her “engaged pedagogy” is thus similar to the “freedom under guidance” implemented in John Dewey’s Laboratory School of Chicago in the late 1890s and early 1900s. It incorporates the dialogue, anchored instruction, and mentoring that Abrami (2015) found to be most effective in improving critical thinking skills and dispositions.

What is the relationship of critical thinking to problem solving, decision-making, higher-order thinking, creative thinking, and other recognized types of thinking? One’s answer to this question obviously depends on how one defines the terms used in the question. If critical thinking is conceived broadly to cover any careful thinking about any topic for any purpose, then problem solving and decision making will be kinds of critical thinking, if they are done carefully. Historically, ‘critical thinking’ and ‘problem solving’ were two names for the same thing. If critical thinking is conceived more narrowly as consisting solely of appraisal of intellectual products, then it will be disjoint with problem solving and decision making, which are constructive.

Bloom’s taxonomy of educational objectives used the phrase “intellectual abilities and skills” for what had been labeled “critical thinking” by some, “reflective thinking” by Dewey and others, and “problem solving” by still others (Bloom et al. 1956: 38). Thus, the so-called “higher-order thinking skills” at the taxonomy’s top levels of analysis, synthesis and evaluation are just critical thinking skills, although they do not come with general criteria for their assessment (Ennis 1981b). The revised version of Bloom’s taxonomy (Anderson et al. 2001) likewise treats critical thinking as cutting across those types of cognitive process that involve more than remembering (Anderson et al. 2001: 269–270). For details, see the Supplement on History .

As to creative thinking, it overlaps with critical thinking (Bailin 1987, 1988). Thinking about the explanation of some phenomenon or event, as in Ferryboat , requires creative imagination in constructing plausible explanatory hypotheses. Likewise, thinking about a policy question, as in Candidate , requires creativity in coming up with options. Conversely, creativity in any field needs to be balanced by critical appraisal of the draft painting or novel or mathematical theory.

• Abrami, Philip C., Robert M. Bernard, Eugene Borokhovski, David I. Waddington, C. Anne Wade, and Tonje Person, 2015, “Strategies for Teaching Students to Think Critically: A Meta-analysis”, Review of Educational Research , 85(2): 275–314. doi:10.3102/0034654314551063
• Aikin, Wilford M., 1942, The Story of the Eight-year Study, with Conclusions and Recommendations , Volume I of Adventure in American Education , New York and London: Harper & Brothers. [ Aikin 1942 available online ]
• Alston, Kal, 1995, “Begging the Question: Is Critical Thinking Biased?”, Educational Theory , 45(2): 225–233. doi:10.1111/j.1741-5446.1995.00225.x
• –––, 2001, “Re/Thinking Critical Thinking: The Seductions of Everyday Life”, Studies in Philosophy and Education , 20(1): 27–40. doi:10.1023/A:1005247128053
• American Educational Research Association, 2014, Standards for Educational and Psychological Testing / American Educational Research Association, American Psychological Association, National Council on Measurement in Education , Washington, DC: American Educational Research Association.
• Anderson, Lorin W., David R. Krathwohl, Peter W. Airiasian, Kathleen A. Cruikshank, Richard E. Mayer, Paul R. Pintrich, James Raths, and Merlin C. Wittrock, 2001, A Taxonomy for Learning, Teaching and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives , New York: Longman, complete edition.
• Bailin, Sharon, 1987, “Critical and Creative Thinking”, Informal Logic , 9(1): 23–30. [ Bailin 1987 available online ]
• –––, 1988, Achieving Extraordinary Ends: An Essay on Creativity , Dordrecht: Kluwer. doi:10.1007/978-94-009-2780-3
• –––, 1995, “Is Critical Thinking Biased? Clarifications and Implications”, Educational Theory , 45(2): 191–197. doi:10.1111/j.1741-5446.1995.00191.x
• Bailin, Sharon and Mark Battersby, 2009, “Inquiry: A Dialectical Approach to Teaching Critical Thinking”, in Juho Ritola (ed.), Argument Cultures: Proceedings of OSSA 09 , CD-ROM (pp. 1–10), Windsor, ON: OSSA. [ Bailin & Battersby 2009 available online ]
• –––, 2016a, “Fostering the Virtues of Inquiry”, Topoi , 35(2): 367–374. doi:10.1007/s11245-015-9307-6
• –––, 2016b, Reason in the Balance: An Inquiry Approach to Critical Thinking , Indianapolis: Hackett, 2nd edition.
• –––, 2021, “Inquiry: Teaching for Reasoned Judgment”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessment , Leiden: Brill, pp. 31–46. doi: 10.1163/9789004444591_003
• Bailin, Sharon, Roland Case, Jerrold R. Coombs, and Leroi B. Daniels, 1999a, “Common Misconceptions of Critical Thinking”, Journal of Curriculum Studies , 31(3): 269–283. doi:10.1080/002202799183124
• –––, 1999b, “Conceptualizing Critical Thinking”, Journal of Curriculum Studies , 31(3): 285–302. doi:10.1080/002202799183133
• Blair, J. Anthony, 2021, Studies in Critical Thinking , Windsor, ON: Windsor Studies in Argumentation, 2nd edition. [Available online at https://windsor.scholarsportal.info/omp/index.php/wsia/catalog/book/106]
• Berman, Alan M., Seth J. Schwartz, William M. Kurtines, and Steven L. Berman, 2001, “The Process of Exploration in Identity Formation: The Role of Style and Competence”, Journal of Adolescence , 24(4): 513–528. doi:10.1006/jado.2001.0386
• Black, Beth (ed.), 2012, An A to Z of Critical Thinking , London: Continuum International Publishing Group.
• Bloom, Benjamin Samuel, Max D. Engelhart, Edward J. Furst, Walter H. Hill, and David R. Krathwohl, 1956, Taxonomy of Educational Objectives. Handbook I: Cognitive Domain , New York: David McKay.
• Boardman, Frank, Nancy M. Cavender, and Howard Kahane, 2018, Logic and Contemporary Rhetoric: The Use of Reason in Everyday Life , Boston: Cengage, 13th edition.
• Browne, M. Neil and Stuart M. Keeley, 2018, Asking the Right Questions: A Guide to Critical Thinking , Hoboken, NJ: Pearson, 12th edition.
• Center for Assessment & Improvement of Learning, 2017, Critical Thinking Assessment Test , Cookeville, TN: Tennessee Technological University.
• Cleghorn, Paul. 2021. “Critical Thinking in the Elementary School: Practical Guidance for Building a Culture of Thinking”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessmen t, Leiden: Brill, pp. 150–167. doi: 10.1163/9789004444591_010
• Cohen, Jacob, 1988, Statistical Power Analysis for the Behavioral Sciences , Hillsdale, NJ: Lawrence Erlbaum Associates, 2nd edition.
• College Board, 1983, Academic Preparation for College. What Students Need to Know and Be Able to Do , New York: College Entrance Examination Board, ERIC document ED232517.
• Commission on the Relation of School and College of the Progressive Education Association, 1943, Thirty Schools Tell Their Story , Volume V of Adventure in American Education , New York and London: Harper & Brothers.
• Council for Aid to Education, 2017, CLA+ Student Guide . Available at http://cae.org/images/uploads/pdf/CLA_Student_Guide_Institution.pdf ; last accessed 2022 07 16.
• Dalgleish, Adam, Patrick Girard, and Maree Davies, 2017, “Critical Thinking, Bias and Feminist Philosophy: Building a Better Framework through Collaboration”, Informal Logic , 37(4): 351–369. [ Dalgleish et al. available online ]
• Dewey, John, 1910, How We Think , Boston: D.C. Heath. [ Dewey 1910 available online ]
• –––, 1916, Democracy and Education: An Introduction to the Philosophy of Education , New York: Macmillan.
• –––, 1933, How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process , Lexington, MA: D.C. Heath.
• –––, 1936, “The Theory of the Chicago Experiment”, Appendix II of Mayhew & Edwards 1936: 463–477.
• –––, 1938, Logic: The Theory of Inquiry , New York: Henry Holt and Company.
• Dominguez, Caroline (coord.), 2018a, A European Collection of the Critical Thinking Skills and Dispositions Needed in Different Professional Fields for the 21st Century , Vila Real, Portugal: UTAD. Available at http://bit.ly/CRITHINKEDUO1 ; last accessed 2022 07 16.
• ––– (coord.), 2018b, A European Review on Critical Thinking Educational Practices in Higher Education Institutions , Vila Real: UTAD. Available at http://bit.ly/CRITHINKEDUO2 ; last accessed 2022 07 16.
• ––– (coord.), 2018c, The CRITHINKEDU European Course on Critical Thinking Education for University Teachers: From Conception to Delivery , Vila Real: UTAD. Available at http:/bit.ly/CRITHINKEDU03; last accessed 2022 07 16.
• Dominguez Caroline and Rita Payan-Carreira (eds.), 2019, Promoting Critical Thinking in European Higher Education Institutions: Towards an Educational Protocol , Vila Real: UTAD. Available at http:/bit.ly/CRITHINKEDU04; last accessed 2022 07 16.
• Ennis, Robert H., 1958, “An Appraisal of the Watson-Glaser Critical Thinking Appraisal”, The Journal of Educational Research , 52(4): 155–158. doi:10.1080/00220671.1958.10882558
• –––, 1962, “A Concept of Critical Thinking: A Proposed Basis for Research on the Teaching and Evaluation of Critical Thinking Ability”, Harvard Educational Review , 32(1): 81–111.
• –––, 1981a, “A Conception of Deductive Logical Competence”, Teaching Philosophy , 4(3/4): 337–385. doi:10.5840/teachphil198143/429
• –––, 1981b, “Eight Fallacies in Bloom’s Taxonomy”, in C. J. B. Macmillan (ed.), Philosophy of Education 1980: Proceedings of the Thirty-seventh Annual Meeting of the Philosophy of Education Society , Bloomington, IL: Philosophy of Education Society, pp. 269–273.
• –––, 1984, “Problems in Testing Informal Logic, Critical Thinking, Reasoning Ability”, Informal Logic , 6(1): 3–9. [ Ennis 1984 available online ]
• –––, 1987, “A Taxonomy of Critical Thinking Dispositions and Abilities”, in Joan Boykoff Baron and Robert J. Sternberg (eds.), Teaching Thinking Skills: Theory and Practice , New York: W. H. Freeman, pp. 9–26.
• –––, 1989, “Critical Thinking and Subject Specificity: Clarification and Needed Research”, Educational Researcher , 18(3): 4–10. doi:10.3102/0013189X018003004
• –––, 1991, “Critical Thinking: A Streamlined Conception”, Teaching Philosophy , 14(1): 5–24. doi:10.5840/teachphil19911412
• –––, 1996, “Critical Thinking Dispositions: Their Nature and Assessability”, Informal Logic , 18(2–3): 165–182. [ Ennis 1996 available online ]
• –––, 1998, “Is Critical Thinking Culturally Biased?”, Teaching Philosophy , 21(1): 15–33. doi:10.5840/teachphil19982113
• –––, 2011, “Critical Thinking: Reflection and Perspective Part I”, Inquiry: Critical Thinking across the Disciplines , 26(1): 4–18. doi:10.5840/inquiryctnews20112613
• –––, 2013, “Critical Thinking across the Curriculum: The Wisdom CTAC Program”, Inquiry: Critical Thinking across the Disciplines , 28(2): 25–45. doi:10.5840/inquiryct20132828
• –––, 2016, “Definition: A Three-Dimensional Analysis with Bearing on Key Concepts”, in Patrick Bondy and Laura Benacquista (eds.), Argumentation, Objectivity, and Bias: Proceedings of the 11th International Conference of the Ontario Society for the Study of Argumentation (OSSA), 18–21 May 2016 , Windsor, ON: OSSA, pp. 1–19. Available at http://scholar.uwindsor.ca/ossaarchive/OSSA11/papersandcommentaries/105 ; last accessed 2022 07 16.
• –––, 2018, “Critical Thinking Across the Curriculum: A Vision”, Topoi , 37(1): 165–184. doi:10.1007/s11245-016-9401-4
• Ennis, Robert H., and Jason Millman, 1971, Manual for Cornell Critical Thinking Test, Level X, and Cornell Critical Thinking Test, Level Z , Urbana, IL: Critical Thinking Project, University of Illinois.
• Ennis, Robert H., Jason Millman, and Thomas Norbert Tomko, 1985, Cornell Critical Thinking Tests Level X & Level Z: Manual , Pacific Grove, CA: Midwest Publication, 3rd edition.
• –––, 2005, Cornell Critical Thinking Tests Level X & Level Z: Manual , Seaside, CA: Critical Thinking Company, 5th edition.
• Ennis, Robert H. and Eric Weir, 1985, The Ennis-Weir Critical Thinking Essay Test: Test, Manual, Criteria, Scoring Sheet: An Instrument for Teaching and Testing , Pacific Grove, CA: Midwest Publications.
• Facione, Peter A., 1990a, Critical Thinking: A Statement of Expert Consensus for Purposes of Educational Assessment and Instruction , Research Findings and Recommendations Prepared for the Committee on Pre-College Philosophy of the American Philosophical Association, ERIC Document ED315423.
• –––, 1990b, California Critical Thinking Skills Test, CCTST – Form A , Millbrae, CA: The California Academic Press.
• –––, 1990c, The California Critical Thinking Skills Test--College Level. Technical Report #3. Gender, Ethnicity, Major, CT Self-Esteem, and the CCTST , ERIC Document ED326584.
• –––, 1992, California Critical Thinking Skills Test: CCTST – Form B, Millbrae, CA: The California Academic Press.
• –––, 2000, “The Disposition Toward Critical Thinking: Its Character, Measurement, and Relationship to Critical Thinking Skill”, Informal Logic , 20(1): 61–84. [ Facione 2000 available online ]
• Facione, Peter A. and Noreen C. Facione, 1992, CCTDI: A Disposition Inventory , Millbrae, CA: The California Academic Press.
• Facione, Peter A., Noreen C. Facione, and Carol Ann F. Giancarlo, 2001, California Critical Thinking Disposition Inventory: CCTDI: Inventory Manual , Millbrae, CA: The California Academic Press.
• Facione, Peter A., Carol A. Sánchez, and Noreen C. Facione, 1994, Are College Students Disposed to Think? , Millbrae, CA: The California Academic Press. ERIC Document ED368311.
• Fisher, Alec, and Michael Scriven, 1997, Critical Thinking: Its Definition and Assessment , Norwich: Centre for Research in Critical Thinking, University of East Anglia.
• Freire, Paulo, 1968 [1970], Pedagogia do Oprimido . Translated as Pedagogy of the Oppressed , Myra Bergman Ramos (trans.), New York: Continuum, 1970.
• Gigerenzer, Gerd, 2001, “The Adaptive Toolbox”, in Gerd Gigerenzer and Reinhard Selten (eds.), Bounded Rationality: The Adaptive Toolbox , Cambridge, MA: MIT Press, pp. 37–50.
• Glaser, Edward Maynard, 1941, An Experiment in the Development of Critical Thinking , New York: Bureau of Publications, Teachers College, Columbia University.
• Groarke, Leo A. and Christopher W. Tindale, 2012, Good Reasoning Matters! A Constructive Approach to Critical Thinking , Don Mills, ON: Oxford University Press, 5th edition.
• Halpern, Diane F., 1998, “Teaching Critical Thinking for Transfer Across Domains: Disposition, Skills, Structure Training, and Metacognitive Monitoring”, American Psychologist , 53(4): 449–455. doi:10.1037/0003-066X.53.4.449
• –––, 2016, Manual: Halpern Critical Thinking Assessment , Mödling, Austria: Schuhfried. Available at https://pdfcoffee.com/hcta-test-manual-pdf-free.html; last accessed 2022 07 16.
• Hamby, Benjamin, 2014, The Virtues of Critical Thinkers , Doctoral dissertation, Philosophy, McMaster University. [ Hamby 2014 available online ]
• –––, 2015, “Willingness to Inquire: The Cardinal Critical Thinking Virtue”, in Martin Davies and Ronald Barnett (eds.), The Palgrave Handbook of Critical Thinking in Higher Education , New York: Palgrave Macmillan, pp. 77–87.
• Haran, Uriel, Ilana Ritov, and Barbara A. Mellers, 2013, “The Role of Actively Open-minded Thinking in Information Acquisition, Accuracy, and Calibration”, Judgment and Decision Making , 8(3): 188–201.
• Hatcher, Donald and Kevin Possin, 2021, “Commentary: Thinking Critically about Critical Thinking Assessment”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessment , Leiden: Brill, pp. 298–322. doi: 10.1163/9789004444591_017
• Haynes, Ada, Elizabeth Lisic, Kevin Harris, Katie Leming, Kyle Shanks, and Barry Stein, 2015, “Using the Critical Thinking Assessment Test (CAT) as a Model for Designing Within-Course Assessments: Changing How Faculty Assess Student Learning”, Inquiry: Critical Thinking Across the Disciplines , 30(3): 38–48. doi:10.5840/inquiryct201530316
• Haynes, Ada and Barry Stein, 2021, “Observations from a Long-Term Effort to Assess and Improve Critical Thinking”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessment , Leiden: Brill, pp. 231–254. doi: 10.1163/9789004444591_014
• Hiner, Amanda L. 2021. “Equipping Students for Success in College and Beyond: Placing Critical Thinking Instruction at the Heart of a General Education Program”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessment , Leiden: Brill, pp. 188–208. doi: 10.1163/9789004444591_012
• Hitchcock, David, 2017, “Critical Thinking as an Educational Ideal”, in his On Reasoning and Argument: Essays in Informal Logic and on Critical Thinking , Dordrecht: Springer, pp. 477–497. doi:10.1007/978-3-319-53562-3_30
• –––, 2021, “Seven Philosophical Implications of Critical Thinking: Themes, Variations, Implications”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessment , Leiden: Brill, pp. 9–30. doi: 10.1163/9789004444591_002
• hooks, bell, 1994, Teaching to Transgress: Education as the Practice of Freedom , New York and London: Routledge.
• –––, 2010, Teaching Critical Thinking: Practical Wisdom , New York and London: Routledge.
• Johnson, Ralph H., 1992, “The Problem of Defining Critical Thinking”, in Stephen P, Norris (ed.), The Generalizability of Critical Thinking , New York: Teachers College Press, pp. 38–53.
• Kahane, Howard, 1971, Logic and Contemporary Rhetoric: The Use of Reason in Everyday Life , Belmont, CA: Wadsworth.
• Kahneman, Daniel, 2011, Thinking, Fast and Slow , New York: Farrar, Straus and Giroux.
• Kahneman, Daniel, Olivier Sibony, & Cass R. Sunstein, 2021, Noise: A Flaw in Human Judgment , New York: Little, Brown Spark.
• Kenyon, Tim, and Guillaume Beaulac, 2014, “Critical Thinking Education and Debasing”, Informal Logic , 34(4): 341–363. [ Kenyon & Beaulac 2014 available online ]
• Krathwohl, David R., Benjamin S. Bloom, and Bertram B. Masia, 1964, Taxonomy of Educational Objectives, Handbook II: Affective Domain , New York: David McKay.
• Kuhn, Deanna, 1991, The Skills of Argument , New York: Cambridge University Press. doi:10.1017/CBO9780511571350
• –––, 2019, “Critical Thinking as Discourse”, Human Development, 62 (3): 146–164. doi:10.1159/000500171
• Lipman, Matthew, 1987, “Critical Thinking–What Can It Be?”, Analytic Teaching , 8(1): 5–12. [ Lipman 1987 available online ]
• –––, 2003, Thinking in Education , Cambridge: Cambridge University Press, 2nd edition.
• Loftus, Elizabeth F., 2017, “Eavesdropping on Memory”, Annual Review of Psychology , 68: 1–18. doi:10.1146/annurev-psych-010416-044138
• Makaiau, Amber Strong, 2021, “The Good Thinker’s Tool Kit: How to Engage Critical Thinking and Reasoning in Secondary Education”, in Daniel Fasko, Jr. and Frank Fair (eds.), Critical Thinking and Reasoning: Theory, Development, Instruction, and Assessment , Leiden: Brill, pp. 168–187. doi: 10.1163/9789004444591_011
• Martin, Jane Roland, 1992, “Critical Thinking for a Humane World”, in Stephen P. Norris (ed.), The Generalizability of Critical Thinking , New York: Teachers College Press, pp. 163–180.
• Mayhew, Katherine Camp, and Anna Camp Edwards, 1936, The Dewey School: The Laboratory School of the University of Chicago, 1896–1903 , New York: Appleton-Century. [ Mayhew & Edwards 1936 available online ]
• McPeck, John E., 1981, Critical Thinking and Education , New York: St. Martin’s Press.
• Moore, Brooke Noel and Richard Parker, 2020, Critical Thinking , New York: McGraw-Hill, 13th edition.
• Nickerson, Raymond S., 1998, “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises”, Review of General Psychology , 2(2): 175–220. doi:10.1037/1089-2680.2.2.175
• Nieto, Ana Maria, and Jorge Valenzuela, 2012, “A Study of the Internal Structure of Critical Thinking Dispositions”, Inquiry: Critical Thinking across the Disciplines , 27(1): 31–38. doi:10.5840/inquiryct20122713
• Norris, Stephen P., 1985, “Controlling for Background Beliefs When Developing Multiple-choice Critical Thinking Tests”, Educational Measurement: Issues and Practice , 7(3): 5–11. doi:10.1111/j.1745-3992.1988.tb00437.x
• Norris, Stephen P. and Robert H. Ennis, 1989, Evaluating Critical Thinking (The Practitioners’ Guide to Teaching Thinking Series), Pacific Grove, CA: Midwest Publications.
• Norris, Stephen P. and Ruth Elizabeth King, 1983, Test on Appraising Observations , St. John’s, NL: Institute for Educational Research and Development, Memorial University of Newfoundland.
• –––, 1984, The Design of a Critical Thinking Test on Appraising Observations , St. John’s, NL: Institute for Educational Research and Development, Memorial University of Newfoundland. ERIC Document ED260083.
• –––, 1985, Test on Appraising Observations: Manual , St. John’s, NL: Institute for Educational Research and Development, Memorial University of Newfoundland.
• –––, 1990a, Test on Appraising Observations , St. John’s, NL: Institute for Educational Research and Development, Memorial University of Newfoundland, 2nd edition.
• –––, 1990b, Test on Appraising Observations: Manual , St. John’s, NL: Institute for Educational Research and Development, Memorial University of Newfoundland, 2nd edition.
• OCR [Oxford, Cambridge and RSA Examinations], 2011, AS/A Level GCE: Critical Thinking – H052, H452 , Cambridge: OCR. Past papers available at https://pastpapers.co/ocr/?dir=A-Level/Critical-Thinking-H052-H452; last accessed 2022 07 16.
• Ontario Ministry of Education, 2013, The Ontario Curriculum Grades 9 to 12: Social Sciences and Humanities . Available at http://www.edu.gov.on.ca/eng/curriculum/secondary/ssciences9to122013.pdf ; last accessed 2022 07 16.
• Passmore, John Arthur, 1980, The Philosophy of Teaching , London: Duckworth.
• Paul, Richard W., 1981, “Teaching Critical Thinking in the ‘Strong’ Sense: A Focus on Self-Deception, World Views, and a Dialectical Mode of Analysis”, Informal Logic , 4(2): 2–7. [ Paul 1981 available online ]
• –––, 1984, “Critical Thinking: Fundamental to Education for a Free Society”, Educational Leadership , 42(1): 4–14.
• –––, 1985, “McPeck’s Mistakes”, Informal Logic , 7(1): 35–43. [ Paul 1985 available online ]
• Paul, Richard W. and Linda Elder, 2006, The Miniature Guide to Critical Thinking: Concepts and Tools , Dillon Beach, CA: Foundation for Critical Thinking, 4th edition.
• Payette, Patricia, and Edna Ross, 2016, “Making a Campus-Wide Commitment to Critical Thinking: Insights and Promising Practices Utilizing the Paul-Elder Approach at the University of Louisville”, Inquiry: Critical Thinking Across the Disciplines , 31(1): 98–110. doi:10.5840/inquiryct20163118
• Possin, Kevin, 2008, “A Field Guide to Critical-Thinking Assessment”, Teaching Philosophy , 31(3): 201–228. doi:10.5840/teachphil200831324
• –––, 2013a, “Some Problems with the Halpern Critical Thinking Assessment (HCTA) Test”, Inquiry: Critical Thinking across the Disciplines , 28(3): 4–12. doi:10.5840/inquiryct201328313
• –––, 2013b, “A Serious Flaw in the Collegiate Learning Assessment (CLA) Test”, Informal Logic , 33(3): 390–405. [ Possin 2013b available online ]
• –––, 2013c, “A Fatal Flaw in the Collegiate Learning Assessment Test”, Assessment Update , 25 (1): 8–12.
• –––, 2014, “Critique of the Watson-Glaser Critical Thinking Appraisal Test: The More You Know, the Lower Your Score”, Informal Logic , 34(4): 393–416. [ Possin 2014 available online ]
• –––, 2020, “CAT Scan: A Critical Review of the Critical-Thinking Assessment Test”, Informal Logic , 40 (3): 489–508. [Available online at https://informallogic.ca/index.php/informal_logic/article/view/6243]
• Rawls, John, 1971, A Theory of Justice , Cambridge, MA: Harvard University Press.
• Rear, David, 2019, “One Size Fits All? The Limitations of Standardised Assessment in Critical Thinking”, Assessment & Evaluation in Higher Education , 44(5): 664–675. doi: 10.1080/02602938.2018.1526255
• Rousseau, Jean-Jacques, 1762, Émile , Amsterdam: Jean Néaulme.
• Scheffler, Israel, 1960, The Language of Education , Springfield, IL: Charles C. Thomas.
• Scriven, Michael, and Richard W. Paul, 1987, Defining Critical Thinking , Draft statement written for the National Council for Excellence in Critical Thinking Instruction. Available at http://www.criticalthinking.org/pages/defining-critical-thinking/766 ; last accessed 2022 07 16.
• Sheffield, Clarence Burton Jr., 2018, “Promoting Critical Thinking in Higher Education: My Experiences as the Inaugural Eugene H. Fram Chair in Applied Critical Thinking at Rochester Institute of Technology”, Topoi , 37(1): 155–163. doi:10.1007/s11245-016-9392-1
• Siegel, Harvey, 1985, “McPeck, Informal Logic and the Nature of Critical Thinking”, in David Nyberg (ed.), Philosophy of Education 1985: Proceedings of the Forty-First Annual Meeting of the Philosophy of Education Society , Normal, IL: Philosophy of Education Society, pp. 61–72.
• –––, 1988, Educating Reason: Rationality, Critical Thinking, and Education , New York: Routledge.
• –––, 1999, “What (Good) Are Thinking Dispositions?”, Educational Theory , 49(2): 207–221. doi:10.1111/j.1741-5446.1999.00207.x
• Simon, Herbert A., 1956, “Rational Choice and the Structure of the Environment”, Psychological Review , 63(2): 129–138. doi: 10.1037/h0042769
• Simpson, Elizabeth, 1966–67, “The Classification of Educational Objectives: Psychomotor Domain”, Illinois Teacher of Home Economics , 10(4): 110–144, ERIC document ED0103613. [ Simpson 1966–67 available online ]
• Skolverket, 2018, Curriculum for the Compulsory School, Preschool Class and School-age Educare , Stockholm: Skolverket, revised 2018. Available at https://www.skolverket.se/download/18.31c292d516e7445866a218f/1576654682907/pdf3984.pdf; last accessed 2022 07 15.
• Smith, B. Othanel, 1953, “The Improvement of Critical Thinking”, Progressive Education , 30(5): 129–134.
• Smith, Eugene Randolph, Ralph Winfred Tyler, and the Evaluation Staff, 1942, Appraising and Recording Student Progress , Volume III of Adventure in American Education , New York and London: Harper & Brothers.
• Splitter, Laurance J., 1987, “Educational Reform through Philosophy for Children”, Thinking: The Journal of Philosophy for Children , 7(2): 32–39. doi:10.5840/thinking1987729
• Stanovich Keith E., and Paula J. Stanovich, 2010, “A Framework for Critical Thinking, Rational Thinking, and Intelligence”, in David D. Preiss and Robert J. Sternberg (eds), Innovations in Educational Psychology: Perspectives on Learning, Teaching and Human Development , New York: Springer Publishing, pp 195–237.
• Stanovich Keith E., Richard F. West, and Maggie E. Toplak, 2011, “Intelligence and Rationality”, in Robert J. Sternberg and Scott Barry Kaufman (eds.), Cambridge Handbook of Intelligence , Cambridge: Cambridge University Press, 3rd edition, pp. 784–826. doi:10.1017/CBO9780511977244.040
• Tankersley, Karen, 2005, Literacy Strategies for Grades 4–12: Reinforcing the Threads of Reading , Alexandria, VA: Association for Supervision and Curriculum Development.
• Thayer-Bacon, Barbara J., 1992, “Is Modern Critical Thinking Theory Sexist?”, Inquiry: Critical Thinking Across the Disciplines , 10(1): 3–7. doi:10.5840/inquiryctnews199210123
• –––, 1993, “Caring and Its Relationship to Critical Thinking”, Educational Theory , 43(3): 323–340. doi:10.1111/j.1741-5446.1993.00323.x
• –––, 1995a, “Constructive Thinking: Personal Voice”, Journal of Thought , 30(1): 55–70.
• –––, 1995b, “Doubting and Believing: Both are Important for Critical Thinking”, Inquiry: Critical Thinking across the Disciplines , 15(2): 59–66. doi:10.5840/inquiryctnews199515226
• –––, 2000, Transforming Critical Thinking: Thinking Constructively , New York: Teachers College Press.
• Toulmin, Stephen Edelston, 1958, The Uses of Argument , Cambridge: Cambridge University Press.
• Turri, John, Mark Alfano, and John Greco, 2017, “Virtue Epistemology”, in Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Winter 2017 Edition). URL = < https://plato.stanford.edu/archives/win2017/entries/epistemology-virtue/ >
• Vincent-Lancrin, Stéphan, Carlos González-Sancho, Mathias Bouckaert, Federico de Luca, Meritxell Fernández-Barrerra, Gwénaël Jacotin, Joaquin Urgel, and Quentin Vidal, 2019, Fostering Students’ Creativity and Critical Thinking: What It Means in School. Educational Research and Innovation , Paris: OECD Publishing.
• Warren, Karen J. 1988. “Critical Thinking and Feminism”, Informal Logic , 10(1): 31–44. [ Warren 1988 available online ]
• Watson, Goodwin, and Edward M. Glaser, 1980a, Watson-Glaser Critical Thinking Appraisal, Form A , San Antonio, TX: Psychological Corporation.
• –––, 1980b, Watson-Glaser Critical Thinking Appraisal: Forms A and B; Manual , San Antonio, TX: Psychological Corporation,
• –––, 1994, Watson-Glaser Critical Thinking Appraisal, Form B , San Antonio, TX: Psychological Corporation.
• Weinstein, Mark, 1990, “Towards a Research Agenda for Informal Logic and Critical Thinking”, Informal Logic , 12(3): 121–143. [ Weinstein 1990 available online ]
• –––, 2013, Logic, Truth and Inquiry , London: College Publications.
• Willingham, Daniel T., 2019, “How to Teach Critical Thinking”, Education: Future Frontiers , 1: 1–17. [Available online at https://prod65.education.nsw.gov.au/content/dam/main-education/teaching-and-learning/education-for-a-changing-world/media/documents/How-to-teach-critical-thinking-Willingham.pdf.]
• Zagzebski, Linda Trinkaus, 1996, Virtues of the Mind: An Inquiry into the Nature of Virtue and the Ethical Foundations of Knowledge , Cambridge: Cambridge University Press. doi:10.1017/CBO9781139174763

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Internet Encyclopedia of Philosophy

Critical thinking.

Critical Thinking is the process of using and assessing reasons to evaluate statements, assumptions, and arguments in ordinary situations. The goal of this process is to help us have good beliefs, where “good” means that our beliefs meet certain goals of thought, such as truth, usefulness, or rationality. Critical thinking is widely regarded as a species of informal logic, although critical thinking makes use of some formal methods. In contrast with formal reasoning processes that are largely restricted to deductive methods—decision theory, logic, statistics—the process of critical thinking allows a wide range of reasoning methods, including formal and informal logic, linguistic analysis, experimental methods of the sciences, historical and textual methods, and philosophical methods, such as Socratic questioning and reasoning by counterexample.

The goals of critical thinking are also more diverse than those of formal reasoning systems. While formal methods focus on deductive validity and truth, critical thinkers may evaluate a statement’s truth, its usefulness, its religious value, its aesthetic value, or its rhetorical value. Because critical thinking arose primarily from the Anglo-American philosophical tradition (also known as “analytic philosophy”), contemporary critical thinking is largely concerned with a statement’s truth. But some thinkers, such as Aristotle (in Rhetoric ), give substantial attention to rhetorical value.

The primary subject matter of critical thinking is the proper use and goals of a range of reasoning methods, how they are applied in a variety of social contexts, and errors in reasoning. This article also discusses the scope and virtues of critical thinking.

Critical thinking should not be confused with Critical Theory. Critical Theory refers to a way of doing philosophy that involves a moral critique of culture. A “critical” theory, in this sense, is a theory that attempts to disprove or discredit a widely held or influential idea or way of thinking in society. Thus, critical race theorists and critical gender theorists offer critiques of traditional views and latent assumptions about race and gender. Critical theorists may use critical thinking methodology, but their subject matter is distinct, and they also may offer critical analyses of critical thinking itself.

Table of Contents

• Argument and Evaluation
• Categorical Logic
• Propositional Logic
• Modal Logic
• Predicate Logic
• Other Formal Systems
• Generalization
• Causal Reasoning
• Formal Fallacies
• Informal Fallacies
• Heuristics and Biases
• The Principle of Charity/Humility
• The Principle of Caution
• The Expansiveness of Critical Thinking
• Productivity and the Limits of Rationality
• Classical Approaches
• The Paul/Elder Model
• Other Approaches
• References and Further Reading

The process of evaluating a statement traditionally begins with making sure we understand it; that is, a statement must express a clear meaning. A statement is generally regarded as clear if it expresses a proposition , which is the meaning the author of that statement intends to express, including definitions, referents of terms, and indexicals, such as subject, context, and time. There is significant controversy over what sort of “entity” propositions are, whether abstract objects or linguistic constructions or something else entirely. Whatever its metaphysical status, it is used here simply to refer to whatever meaning a speaker intends to convey in a statement.

The difficulty with identifying intended propositions is that we typically speak and think in natural languages (English, Swedish, French), and natural languages can be misleading. For instance, two different sentences in the same natural language may express the same proposition, as in these two English sentences:

Jamie is taller than his father. Jamie’s father is shorter than he.

Further, the same sentence in a natural language can express more than one proposition depending on who utters it at a time:

I am shorter than my father right now.

The pronoun “I” is an indexical; it picks out, or “indexes,” whoever utters the sentence and, therefore, expresses a different proposition for each new speaker who utters it. Similarly, “right now” is a temporal indexical; it indexes the time the sentence is uttered. The proposition it is used to express changes each new time the sentence is uttered and, therefore, may have a different truth value at different times (as, say, the speaker grows taller: “I am now five feet tall” may be true today, but false a year from now). Other indexical terms that can affect the meaning of the sentence include other pronouns (he, she, it) and definite articles (that, the).

Further still, different sentences in different natural languages may express the same proposition . For example, all of the following express the proposition “Snow is white”:

Snow is white. (English)

Der Schnee ist weiss. (German)

La neige est blanche. (French)

La neve é bianca. (Italian)

Finally, statements in natural languages are often vague or ambiguous , either of which can obscure the propositions actually intended by their authors. And even in cases where they are not vague or ambiguous, statements’ truth values sometimes vary from context to context. Consider the following example.

The English statement, “It is heavy,” includes the pronoun “it,” which (when used without contextual clues) is ambiguous because it can index any impersonal subject. If, in this case, “it” refers to the computer on which you are reading this right now, its author intends to express the proposition, “The computer on which you are reading this right now is heavy.” Further, the term “heavy” reflects an unspecified standard of heaviness (again, if contextual clues are absent). Assuming we are talking about the computer, it may be heavy relative to other computer models but not to automobiles. Further still, even if we identify or invoke a standard of heaviness by which to evaluate the appropriateness of its use in this context, there may be no weight at which an object is rightly regarded as heavy according to that standard. (For instance, is an object heavy because it weighs 5.3 pounds but not if it weighs 5.2 pounds? Or is it heavy when it is heavier than a mouse but lighter than an anvil?) This means “heavy” is a vague term. In order to construct a precise statement, vague terms (heavy, cold, tall) must often be replaced with terms expressing an objective standard (pounds, temperature, feet).

Part of the challenge of critical thinking is to clearly identify the propositions (meanings) intended by those making statements so we can effectively reason about them. The rules of language help us identify when a term or statement is ambiguous or vague, but they cannot, by themselves, help us resolve ambiguity or vagueness. In many cases, this requires assessing the context in which the statement is made or asking the author what she intends by the terms. If we cannot discern the meaning from the context and we cannot ask the author, we may stipulate a meaning, but this requires charity, to stipulate a plausible meaning, and humility, to admit when we discover that our stipulation is likely mistaken.

2. Argument and Evaluation

Once we are satisfied that a statement is clear, we can begin evaluating it. A statement can be evaluated according to a variety of standards. Commonly, statements are evaluated for truth, usefulness, or rationality. The most common of these goals is truth, so that is the focus of this article.

The truth of a statement is most commonly evaluated in terms of its relation to other statements and direct experiences. If a statement follows from or can be inferred from other statements that we already have good reasons to believe, then we have a reason to believe that statement. For instance, the statement “The ball is blue” can be derived from “The ball is blue and round.” Similarly, if a statement seems true in light of, or is implied by, an experience, then we have a reason to believe that statement. For instance, the experience of seeing a red car is a reason to believe, “The car is red.” (Whether these reasons are good enough for us to believe is a further question about justification , which is beyond the scope of this article, but see “ Epistemic Justification .”) Any statement we derive in these ways is called a conclusion . Though we regularly form conclusions from other statements and experiences—often without thinking about it—there is still a question of whether these conclusions are true: Did we draw those conclusions well? A common way to evaluate the truth of a statement is to identify those statements and experiences that support our conclusions and organize them into structures called arguments . (See also, “ Argument .”)

An argument is one or more statements (called premises ) intended to support the truth of another statement (the conclusion ). Premises comprise the evidence offered in favor of the truth of a conclusion. It is important to entertain any premises that are intended to support a conclusion, even if the attempt is unsuccessful. Unsuccessful attempts at supporting a proposition constitute bad arguments, but they are still arguments. The support intended for the conclusion may be formal or informal. In a formal, or deductive, argument, an arguer intends to construct an argument such that, if the premises are true, the conclusion must be true. This strong relationship between premises and conclusion is called validity . This relationship between the premises and conclusion is called “formal” because it is determined by the form (that is, the structure) of the argument (see §3). In an informal, or inductive , argument, the conclusion may be false even if the premises are true. In other words, whether an inductive argument is good depends on something more than the form of the argument. Therefore, all inductive arguments are invalid, but this does not mean they are bad arguments. Even if an argument is invalid, its premises can increase the probability that its conclusion is true. So, the form of inductive arguments is evaluated in terms of the strength the premises confer on the conclusion, and stronger inductive arguments are preferred to weaker ones (see §4). (See also, “ Deductive and Inductive Arguments .”)

Psychological states, such as sensations, memories, introspections, and intuitions often constitute evidence for statements. Although these states are not themselves statements, they can be expressed as statements. And when they are, they can be used in and evaluated by arguments. For instance, my seeing a red wall is evidence for me that, “There is a red wall,” but the physiological process of seeing is not a statement. Nevertheless, the experience of seeing a red wall can be expressed as the proposition, “I see a red wall” and can be included in an argument such as the following:

• I see a red wall in front of me.
• Therefore, there is a red wall in front of me.

This is an inductive argument, though not a strong one. We do not yet know whether seeing something (under these circumstances) is reliable evidence for the existence of what I am seeing. Perhaps I am “seeing” in a dream, in which case my seeing is not good evidence that there is a wall. For similar reasons, there is also reason to doubt whether I am actually seeing. To be cautious, we might say we seem to see a red wall.

To be good , an argument must meet two conditions: the conclusion must follow from the premises—either validly or with a high degree of likelihood—and the premises must be true. If the premises are true and the conclusion follows validly, the argument is sound . If the premises are true and the premises make the conclusion probable (either objectively or relative to alternative conclusions), the argument is cogent .

Here are two examples:

• Earth is larger than its moon.
• Our sun is larger than Earth.
• Therefore, our sun is larger than Earth’s moon.

In example 1, the premises are true. And since “larger than” is a transitive relation, the structure of the argument guarantees that, if the premises are true, the conclusion must be true. This means the argument is also valid. Since it is both valid and has true premises, this deductive argument is sound.

  Example 2:

• It is sunny in Montana about 205 days per year.
• I will be in Montana in February.
• Hence, it will probably be sunny when I am in Montana.

In example 2, premise 1 is true, and let us assume premise 2 is true. The phrase “almost always” indicates that a majority of days in Montana are sunny, so that, for any day you choose, it will probably be a sunny day. Premise 2 says I am choosing days in February to visit. Together, these premises strongly support (though they do not guarantee) the conclusion that it will be sunny when I am there, and so this inductive argument is cogent.

In some cases, arguments will be missing some important piece, whether a premise or a conclusion. For instance, imagine someone says, “Well, she asked you to go, so you have to go.” The idea that you have to go does not follow logically from the fact that she asked you to go without more information. What is it about her asking you to go that implies you have to go? Arguments missing important information are called enthymemes . A crucial part of critical thinking is identifying missing or assumed information in order to effectively evaluate an argument. In this example, the missing premise might be that, “She is your boss, and you have to do what she asks you to do.” Or it might be that, “She is the woman you are interested in dating, and if you want a real chance at dating her, you must do what she asks.” Before we can evaluate whether her asking implies that you have to go, we need to know this missing bit of information. And without that missing bit of information, we can simply reply, “That conclusion doesn’t follow from that premise.”

The two categories of reasoning associated with soundness and cogency—formal and informal, respectively—are considered, by some, to be the only two types of argument. Others add a third category, called abductive reasoning, according to which one reasons according to the rules of explanation rather than the rules of inference . Those who do not regard abductive reasoning as a third, distinct category typically regard it as a species of informal reasoning. Although abductive reasoning has unique features, here it is treated, for reasons explained in §4d, as a species of informal reasoning, but little hangs on this characterization for the purposes of this article.

3. Formal Reasoning

Although critical thinking is widely regarded as a type of informal reasoning, it nevertheless makes substantial use of formal reasoning strategies. Formal reasoning is deductive , which means an arguer intends to infer or derive a proposition from one or more propositions on the basis of the form or structure exhibited by the premises. Valid argument forms guarantee that particular propositions can be derived from them. Some forms look like they make such guarantees but fail to do so (we identify these as formal fallacies in §5a). If an arguer intends or supposes that a premise or set of premises guarantee a particular conclusion, we may evaluate that argument form as deductive even if the form fails to guarantee the conclusion, and is thus discovered to be invalid.

Before continuing in this section, it is important to note that, while formal reasoning provides a set of strict rules for drawing valid inferences, it cannot help us determine the truth of many of our original premises or our starting assumptions. And in fact, very little critical thinking that occurs in our daily lives (unless you are a philosopher, engineer, computer programmer, or statistician) involves formal reasoning. When we make decisions about whether to board an airplane, whether to move in with our significant others, whether to vote for a particular candidate, whether it is worth it to drive ten miles faster the speed limit even if I am fairly sure I will not get a ticket, whether it is worth it to cheat on a diet, or whether we should take a job overseas, we are reasoning informally. We are reasoning with imperfect information (I do not know much about my flight crew or the airplane’s history), with incomplete information (no one knows what the future is like), and with a number of built-in biases, some conscious (I really like my significant other right now), others unconscious (I have never gotten a ticket before, so I probably will not get one this time). Readers who are more interested in these informal contexts may want to skip to §4.

An argument form is a template that includes variables that can be replaced with sentences. Consider the following form (found within the formal system known as sentential logic ):

• If p, then q.
• Therefore, q.

This form was named modus ponens (Latin, “method of putting”) by medieval philosophers. p and q are variables that can be replaced with any proposition, however simple or complex. And as long as the variables are replaced consistently (that is, each instance of p is replaced with the same sentence and the same for q ), the conclusion (line 3), q , follows from these premises. To be more precise, the inference from the premises to the conclusion is valid . “Validity” describes a particular relationship between the premises and the conclusion, namely: in all cases , the conclusion follows necessarily from the premises, or, to use more technical language, the premises logically guarantee an instance of the conclusion.

Notice we have said nothing yet about truth . As critical thinkers, we are interested, primarily, in evaluating the truth of sentences that express propositions, but all we have discussed so far is a type of relationship between premises and conclusion (validity). This formal relationship is analogous to grammar in natural languages and is known in both fields as syntax . A sentence is grammatically correct if its syntax is appropriate for that language (in English, for example, a grammatically correct simple sentence has a subject and a predicate—“He runs.” “Laura is Chairperson.”—and it is grammatically correct regardless of what subject or predicate is used—“Jupiter sings.”—and regardless of whether the terms are meaningful—“Geflorble rowdies.”). Whether a sentence is meaningful, and therefore, whether it can be true or false, depends on its semantics , which refers to the meaning of individual terms (subjects and predicates) and the meaning that emerges from particular orderings of terms. Some terms are meaningless—geflorble; rowdies—and some orderings are meaningless even though their terms are meaningful—“Quadruplicity drinks procrastination,” and “Colorless green ideas sleep furiously.”.

Despite the ways that syntax and semantics come apart, if sentences are meaningful, then syntactic relationships between premises and conclusions allow reasoners to infer truth values for conclusions. Because of this, a more common definition of validity is this: it is not possible for all the premises to be true and the conclusion false . Formal logical systems in which syntax allows us to infer semantic values are called truth-functional or truth-preserving —proper syntax preserves truth throughout inferences.

The point of this is to note that formal reasoning only tells us what is true if we already know our premises are true. It cannot tell us whether our experiences are reliable or whether scientific experiments tell us what they seem to tell us. Logic can be used to help us determine whether a statement is true, but only if we already know some true things. This is why a broad conception of critical thinking is so important: we need many different tools to evaluate whether our beliefs are any good.

Consider, again, the form modus ponens , and replace p with “It is a cat” and q with “It is a mammal”:

• If it is a cat, then it is a mammal.
• It is a cat.
• Therefore, it is a mammal.

In this case, we seem to “see” (in a metaphorical sense of see ) that the premises guarantee the truth of the conclusion. On reflection, it is also clear that the premises might not be true; for instance, if “it” picks out a rock instead of a cat, premise 1 is still true, but premise 2 is false. It is also possible for the conclusion to be true when the premises are false. For instance, if the “it” picks out a dog instead of a cat, the conclusion “It is a mammal” is true. But in that case, the premises do not guarantee that conclusion; they do not constitute a reason to believe the conclusion is true.

Summing up, an argument is valid if its premises logically guarantee an instance of its conclusion (syntactically), or if it is not possible for its premises to be true and its conclusion false (semantically). Logic is truth-preserving but not truth-detecting; we still need evidence that our premises are true to use logic effectively.

            A Brief Technical Point

Some readers might find it worth noting that the semantic definition of validity has two counterintuitive consequences. First, it implies that any argument with a necessarily true conclusion is valid. Notice that the condition is phrased hypothetically: if the premises are true, then the conclusion cannot be false. This condition is met if the conclusion cannot be false:

• Two added to two equals four.

This is because the hypothetical (or “conditional”) statement would still be true even if the premises were false:

• If it is blue, then it flies.
• It is an airplane.

It is true of this argument that if the premises were true, the conclusion would be since the conclusion is true no matter what.

Second, the semantic formulation also implies that any argument with necessarily false premises is valid. The semantic condition for validity is met if the premises cannot be true:

• Some bachelors are married.
• Earth’s moon is heavier than Jupiter.

In this case, if the premise were true, the conclusion could not be false (this is because anything follows syntactically from a contradiction), and therefore, the argument is valid. There is nothing particularly problematic about these two consequences. But they highlight unexpected implications of our standard formulations of validity, and they show why there is more to good arguments than validity.

Despite these counterintuitive implications, valid reasoning is essential to thinking critically because it is a truth-preserving strategy: if deductive reasoning is applied to true premises, true conclusions will result.

There are a number of types of formal reasoning, but here we review only some of the most common: categorical logic, propositional logic, modal logic, and predicate logic.

a. Categorical Logic

Categorical logic is formal reasoning about categories or collections of subjects, where subjects refers to anything that can be regarded as a member of a class, whether objects, properties, or events or even a single object, property, or event. Categorical logic employs the quantifiers “all,” “some,” and “none” to refer to the members of categories, and categorical propositions are formulated in four ways:

A claims: All As are Bs (where the capitals “A” and “B” represent categories of subjects).

E claims: No As are Bs.

I claims: Some As are Bs.

O claims: Some As are not Bs.

Categorical syllogisms are syllogisms (two-premised formal arguments) that employ categorical propositions. Here are two examples:

• All cats are mammals. (A claim) 1. No bachelors are married. (E claim)
• Some cats are furry. (I claim) 2. All the people in this building are bachelors. (A claim)
• Therefore, some mammals are furry. (I claim) 3. Thus, no people in this building are married. (E claim)

There are interesting limitations on what categorical logic can do. For instance, if one premise says that, “Some As are not Bs,” may we infer that some As are Bs, in what is known as an “existential assumption”? Aristotle seemed to think so ( De Interpretatione ), but this cannot be decided within the rules of the system. Further, and counterintuitively, it would mean that a proposition such as, “Some bachelors are not married,” is false since it implies that some bachelors are married.

Another limitation on categorical logic is that arguments with more than three categories cannot be easily evaluated for validity. The standard method for evaluating the validity of categorical syllogisms is the Venn diagram (named after John Venn, who introduced it in 1881), which expresses categorical propositions in terms of two overlapping circles and categorical arguments in terms of three overlapping circles, each circle representing a category of subjects.

Venn diagram for claim and Venn diagram for argument

A, B, and C represent categories of objects, properties, or events. The symbol “ ∩ ” comes from mathematical set theory to indicate “intersects with.” “A∩B” means all those As that are also Bs and vice versa. 

Though there are ways of constructing Venn diagrams with more than three categories, determining the validity of these arguments using Venn diagrams is very difficult (and often requires computers). These limitations led to the development of more powerful systems of formal reasoning.

b. Propositional Logic

Propositional, or sentential , logic has advantages and disadvantages relative to categorical logic. It is more powerful than categorical logic in that it is not restricted in the number of terms it can evaluate, and therefore, it is not restricted to the syllogistic form. But it is weaker than categorical logic in that it has no operators for quantifying over subjects, such as “all” or “some.” For those, we must appeal to predicate logic (see §3c below).

Basic propositional logic involves formal reasoning about propositions (as opposed to categories), and its most basic unit of evaluation is the atomic proposition . “Atom” means the smallest indivisible unit of something, and simple English statements (subject + predicate) are atomic wholes because if either part is missing, the word or words cease to be a statement, and therefore ceases to be capable of expressing a proposition. Atomic propositions are simple subject-predicate combinations, for instance, “It is a cat” and “I am a mammal.” Variable letters such as p and q in argument forms are replaced with semantically rich constants, indicated by capital letters, such as A and B . Consider modus ponens again (noting that the atomic propositions are underlined in the English argument):

As you can see from premise 1 of the Semantic Replacement, atomic propositions can be combined into more complex propositions using symbols that represent their logical relationships (such as “If…, then…”). These symbols are called “operators” or “connectives.” The five standard operators in basic propositional logic are:

These operations allow us to identify valid relations among propositions: that is, they allow us to formulate a set of rules by which we can validly infer propositions from and validly replace them with others. These rules of inference (such as modus ponens ; modus tollens ; disjunctive syllogism) and rules of replacement (such as double negation; contraposition; DeMorgan’s Law) comprise the syntax of propositional logic, guaranteeing the validity of the arguments employing them.

Two Rules of Inference:

Two Rules of Replacement:

For more, see “ Propositional Logic .”

c. Modal Logic

Standard propositional logic does not capture every type of proposition we wish to express (recall that it does not allow us to evaluate categorical quantifiers such as “all” or “some”). It also does not allow us to evaluate propositions expressed as possibly true or necessarily true, modifications that are called modal operators or modal quantifiers .

Modal logic refers to a family of formal propositional systems, the most prominent of which includes operators for necessity (□) and possibility (◊) (see §3d below for examples of other modal systems). If a proposition, p , is possibly true, ◊ p , it may or may not be true. If p is necessarily true, □ p , it must be true; it cannot be false. If p is necessarily false, either ~◊ p or □~ p , it must be false; it cannot be true.

There is a variety of modal systems, the weakest of which is called K (after Saul Kripke, who exerted important influence on the development of modal logic), and it involves only two additional rules:

Necessitation Rule:   If  A  is a theorem of  K , then so is □ A .

Distribution Axiom:  □( A ⊃ B ) ⊃ (□ A ⊃□ B ).  [If it is necessarily the case that if A, then B , then if it is necessarily the case that A, it is necessarily the case that B .]

Other systems maintain these rules and add others for increasing strength. For instance, the (S4) modal system includes axiom (4):

(4)  □ A ⊃ □□ A   [If it is necessarily the case that A, then it is necessarily necessary that A.]

An influential and intuitive way of thinking about modal concepts is the idea of “possible worlds” (see Plantinga, 1974; Lewis 1986). A world is just the set of all true propositions. The actual world is the set of all actually true propositions—everything that was true, is true, and (depending on what you believe about the future) will be true. A possible world is a way the actual world might have been. Imagine you wore green underwear today. The actual world might have been different in that way: you might have worn blue underwear. In this interpretation of modal quantifiers, there is a possible world in which you wore blue underwear instead of green underwear. And for every possibility like this, and every combination of those possibilities, there is a distinct possible world.

If a proposition is not possible, then there is no possible world in which that proposition is true. The statement, “That object is red all over and blue all over at the same time” is not true in any possible worlds. Therefore, it is not possible (~◊P), or, in other words, necessarily false (□~P). If a proposition is true in all possible worlds, it is necessarily true. For instance, the proposition, “Two plus two equal four,” is true in all possible worlds, so it is necessarily true (□P) or not possibly false (~◊~P).

All modal systems have a number of controversial implications, and there is not space to review them here. Here we need only note that modal logic is a type of formal reasoning that increases the power of propositional logic to capture more of what we attempt to express in natural languages. (For more, see “ Modal Logic: A Contemporary View .”)

d. Predicate Logic

Predicate logic, in particular, first-order predicate logic, is even more powerful than propositional logic. Whereas propositional logic treats propositions as atomic wholes, predicate logic allows reasoners to identify and refer to subjects of propositions, independently of their predicates. For instance, whereas the proposition, “Susan is witty,” would be replaced with a single upper-case letter, say “S,” in propositional logic, predicate logic would assign the subject “Susan” a lower-case letter, s, and the predicate “is witty” an upper-case letter, W, and the translation (or formula ) would be: Ws.

In addition to distinguishing subjects and predicates, first-order predicate logic allows reasoners to quantify over subjects. The quantifiers in predicate logic are “All…,” which is comparable to “All” quantifier in categorical logic and is sometimes symbolized with an upside-down A: ∀ (though it may not be symbolized at all), and “There is at least one…,” which is comparable to “Some” quantifier in categorical logic and is symbolized with a backward E: ∃. E and O claims are formed by employing the negation operator from propositional logic. In this formal system, the proposition, “Someone is witty,” for example, has the form: There is an x , such that x has the property of being witty, which is symbolized: (∃ x)(Wx). Similarly, the proposition, “Everyone is witty,” has the form: For all x, x has the property of being witty, which is symbolized (∀ x )( Wx ) or, without the ∀: ( x )( Wx ).

Predicate derivations are conducted according to the same rules of inference and replacement as propositional logic with the exception of four rules to accommodate adding and eliminating quantifiers.

Second-order predicate logic extends first-order predicate logic to allow critical thinkers to quantify over and draw inferences about subjects and predicates, including relations among subjects and predicates. In both first- and second-order logic, predicates typically take the form of properties (one-place predicates) or relations (two-place predicates), though there is no upper limit on place numbers. Second-order logic allows us to treat both as falling under quantifiers, such as e verything that is (specifically, that has the property of being) a tea cup and everything that is a bachelor is unmarried .

e. Other Formal Systems

It is worth noting here that the formal reasoning systems we have seen thus far (categorical, propositional, and predicate) all presuppose that truth is bivalent , that is, two-valued. The two values critical thinkers are most often concerned with are true and false , but any bivalent system is subject to the rules of inference and replacement of propositional logic. The most common alternative to truth values is the binary code of 1s and 0s used in computer programming. All logics that presuppose bivalence are called classical logics . In the next section, we see that not all formal systems are bivalent; there are non-classical logics . The existence of non-classical systems raises interesting philosophical questions about the nature of truth and the legitimacy of our basic rules of reasoning, but these questions are too far afield for this context. Many philosophers regard bivalent systems as legitimate for all but the most abstract and purely formal contexts. Included below is a brief description of three of the most common non-classical logics.

Tense logic , or temporal logic, is a formal modal system developed by Arthur Prior (1957, 1967, 1968) to accommodate propositional language about time. For example, in addition to standard propositional operators, tense logic includes four operators for indexing times: P “It has at some time been the case that…”; F “It will at some time be the case that…”; H “It has always been the case that…”; and G “It will always be the case that….”

Many-valued logic , or n -valued logic, is a family of formal logical systems that attempts to accommodate intuitions that suggest some propositions have values in addition to true and false. These are often motivated by intuitions that some propositions have neither of the classic truth values; their truth value is indeterminate (not just undeterminable, but neither true nor false), for example, propositions about the future such as, “There will be a sea battle tomorrow.” If the future does not yet exist, there is no fact about the future, and therefore, nothing for a proposition to express.

Fuzzy logic is a type of many-valued logic developed out of Lotfi Zadeh’s (1965) work on mathematical sets. Fuzzy logic attempts to accommodate intuitions that suggest some propositions have truth value in degrees, that is, some degree of truth between true and false. It is motivated by concerns about vagueness in reality, for example whether a certain color is red or some degree of red, or whether some temperature is hot or some degree of hotness.

Formal reasoning plays an important role in critical thinking, but not very often. There are significant limits to how we might use formal tools in our daily lives. If that is true, how do critical thinkers reason well when formal reasoning cannot help? That brings us to informal reasoning.

4. Informal Reasoning

Informal reasoning is inductive , which means that a proposition is inferred (but not derived) from one or more propositions on the basis of the strength provided by the premises (where “strength” means some degree of likelihood less than certainty or some degree of probability less than 1 but greater than 0; a proposition with 0% probability is necessarily false).

Particular premises grant strength to premises to the degree that they reflect certain relationships or structures in the world . For instance, if a particular type of event, p , is known to cause or indicate another type of event, q , then upon encountering an event of type p , we may infer that an event of type q is likely to occur. We may express this relationship among events propositionally as follows:

• Events of type p typically cause or indicate events of type q .
• An event of type p occurred.
• Therefore, an event of type q probably occurred.

If the structure of the world (for instance, natural laws) makes premise 1 true, then, if premise 2 is true, we can reasonably (though not certainly) infer the conclusion.

Unlike formal reasoning, the adequacy of informal reasoning depends on how well the premises reflect relationships or structures in the world. And since we have not experienced every relationship among objects or events or every structure, we cannot infer with certainty that a particular conclusion follows from a true set of premises about these relationships or structures. We can only infer them to some degree of likelihood by determining to the best of our ability either their objective probability or their probability relative to alternative conclusions.

The objective probability of a conclusion refers to how likely, given the way the world is regardless of whether we know it , that conclusion is to be true. The epistemic probability of a conclusion refers to how likely that conclusion is to be true given what we know about the world , or more precisely, given our evidence for its objective likelihood.

Objective probabilities are determined by facts about the world and they are not truths of logic, so we often need evidence for objective probabilities. For instance, imagine you are about to draw a card from a standard playing deck of 52 cards. Given particular assumptions about the world (that this deck contains 52 cards and that one of them is the Ace of Spades), the objective likelihood that you will draw an Ace of Spades is 1/52. These assumptions allow us to calculate the objective probability of drawing an Ace of Spades regardless of whether we have ever drawn a card before. But these are assumptions about the world that are not guaranteed by logic: we have to actually count the cards, to be sure we count accurately and are not dreaming or hallucinating, and that our memory (once we have finished counting) reliably maintains our conclusions. None of these processes logically guarantees true beliefs. So, if our assumptions are correct, we know the objective probability of actually drawing an Ace of Spades in the real world. But since there is no logical guarantee that our assumptions are right, we are left only with the epistemic probability (the probability based on our evidence) of drawing that card. If our assumptions are right, then the objective probability is the same as our epistemic probability: 1/52. But even if we are right, objective and epistemic probabilities can come apart under some circumstances.

Imagine you draw a card without looking at it and lay it face down. What is the objective probability that that card is an Ace of Spades? The structure of the world has now settled the question, though you do not know the outcome. If it is an Ace of Spades, the objective probability is 1 (100%); it is the Ace of Spades. If it is not the Ace of Spades, the objective probability is 0 (0%); it is not the Ace of Spades. But what is the epistemic probability? Since you do not know any more about the world than you did before you drew the card, the epistemic probability is the same as before you drew it: 1/52.

Since much of the way the world is is hidden from us (like the card laid face down), and since it is not obvious that we perceive reality as it actually is (we do not know whether the actual coins we flip are evenly weighted or whether the actual dice we roll are unbiased), our conclusions about probabilities in the actual world are inevitably epistemic probabilities. We can certainly calculate objective probabilities about abstract objects (for instance, hypothetically fair coins and dice—and these calculations can be evaluated formally using probability theory and statistics), but as soon as we apply these calculations to the real world, we must accommodate the fact that our evidence is incomplete.

There are four well-established categories of informal reasoning: generalization, analogy, causal reasoning, and abduction.

a. Generalization

Generalization is a way of reasoning informally from instances of a type to a conclusion about the type. This commonly takes two forms: reasoning from a sample of a population to the whole population , and reasoning from past instances of an object or event to future instances of that object or event . The latter is sometimes called “enumerative induction” because it involves enumerating past instances of a type in order to draw an inference about a future instance. But this distinction is weak; both forms of generalization use past or current data to infer statements about future instances and whole current populations.

A popular instance of inductive generalization is the opinion poll: a sample of a population of people is polled with respect to some statement or belief. For instance, if we poll 57 sophomores enrolled at a particular college about their experiences of living in dorms, these 57 comprise our sample of the population of sophomores at that particular college. We want to be careful how we define our population given who is part of our sample. Not all college students are like sophomores, so it is not prudent to draw inferences about all college students from these sophomores. Similarly, sophomores at other colleges are not necessarily like sophomores at this college (it could be the difference between a liberal arts college and a research university), so it is prudent not to draw inferences about all sophomores from this sample at a particular college.

Let us say that 90% of the 57 sophomores we polled hate the showers in their dorms. From this information, we might generalize in the following way:

• We polled 57 sophomores at Plato’s Academy. (the sample)
• 90% of our sample hates the showers in their dorms. (the polling data)
• Therefore, probably 90% of all sophomores at Plato’s Academy hate the showers in their dorms. (a generalization from our sample to the whole population of sophomores at Plato’s Academy)

Is this good evidence that 90% of all sophomores at that college hate the showers in their dorms?

A generalization is typically regarded as a good argument if its sample is representative of its population. A sample is representative if it is similar in the relevant respects to its population. A perfectly representative sample would include the whole population: the sample would be identical with the population, and thus, perfectly representative. In that case, no generalization is necessary. But we rarely have the time or resources to evaluate whole populations. And so, a sample is generally regarded as representative if it is large relative to its population and unbiased .

In our example, whether our inference is good depends, in part, on how many sophomores there are. Are there 100, 2,000? If there are only 100, then our sample size seems adequate—we have polled over half the population. Is our sample unbiased? That depends on the composition of the sample. Is it comprised only of women or only of men? If this college is not co-ed, that is not a problem. But if the college is co-ed and we have sampled only women, our sample is biased against men. We have information only about female freshmen dorm experiences, and therefore, we cannot generalize about male freshmen dorm experiences.

How large is large enough? This is a difficult question to answer. A poll of 1% of your high school does not seem large enough to be representative. You should probably gather more data. Yet a poll of 1% of your whole country is practically impossible (you are not likely to ever have enough grant money to conduct that poll). But could a poll of less than 1% be acceptable? This question is not easily answered, even by experts in the field. The simple answer is: the more, the better. The more complicated answer is: it depends on how many other factors you can control for, such as bias and hidden variables (see §4c for more on experimental controls).

Similarly, we might ask what counts as an unbiased sample. An overly simple answer is: the sample is taken randomly, that is, by using a procedure that prevents consciously or unconsciously favoring one segment of the population over another (flipping a coin, drawing lottery balls). But reality is not simple. In political polls, it is important not to use a selection procedure that results in a sample with a larger number of members of one political party than another relative to their distribution in the population, even if the resulting sample is random. For example, the two most prominent parties in the U.S. are the Democratic Party and the Republican Party. If 47% of the U.S. is Republican and 53% is Democrat, an unbiased sample would have approximately 47% Republicans and 53% Democrats. But notice that simply choosing at random may not guarantee that result; it could easily occur, just by choosing randomly, that our sample has 70% Democrats and 30% Republicans (suppose our computer chose, albeit randomly, from a highly Democratic neighborhood). Therefore, we want to control for representativeness in some criteria, such as gender, age, and education. And we explicitly want to avoid controlling for the results we are interested in; if we controlled for particular answers to the questions on our poll, we would not learn anything—we would get all and only the answers we controlled for.

Difficulties determining representativeness suggest that reliable generalizations are not easy to construct. If we generalize on the basis of samples that are too small or if we cannot control for bias, we commit the informal fallacy of hasty generalization (see §5b). In order to generalize well, it seems we need a bit of machinery to guarantee representativeness. In fact, it seems we need an experiment, one of the primary tools in causal reasoning (see §4c below).

Argument from Analogy , also called analogical reasoning , is a way of reasoning informally about events or objects based on their similarities. A classic instance of reasoning by analogy occurs in archaeology, when researchers attempt to determine whether a stone object is an artifact (a human-made item) or simply a rock. By comparing the features of an unknown stone with well-known artifacts, archaeologists can infer whether a particular stone is an artifact. Other examples include identifying animals’ tracks by their similarities with pictures in a guidebook and consumer reports on the reliability of products.

To see how arguments from analogy work in detail, imagine two people who, independently of one another, want to buy a new pickup truck. Each chooses a make and model he or she likes, and let us say they decide on the same truck. They then visit a number of consumer reporting websites to read reports on trucks matching the features of the make and model they chose, for instance, the year it was built, the size of the engine (6 cyl. or 8 cyl.), the type of transmission (2WD or 4WD), the fuel mileage, and the cab size (standard, extended, crew). Now, let us say one of our prospective buyers is interested in safety —he or she wants a tough, safe vehicle that will protect against injuries in case of a crash. The other potential buyer is interested in mechanical reliability —he or she does not want to spend a lot of time and money fixing mechanical problems.

With this in mind, here is how our two buyers might reason analogically about whether to purchase the truck (with some fake report data included):

• The truck I have in mind was built in 2012, has a 6-cylinder engine, a 2WD transmission, and a king cab.
• 62 people who bought trucks like this one posted consumer reports and have driven it for more than a year.
• 88% of those 62 people report that the truck feels very safe.
• Therefore, the truck I am looking at will likely be very safe.
• 88% of those 62 people report that the truck has had no mechanical problems.
• Therefore, the truck I am looking at will likely have no mechanical problems.

Are the features of these analogous vehicles (the ones reported on) sufficiently numerous and relevant for helping our prospective truck buyers decide whether to purchase the truck in question (the one on the lot)? Since we have some idea that the type of engine and transmission in a vehicle contribute to its mechanical reliability, Buyer 2 may have some relevant features on which to draw a reliable analogy. Fuel mileage and cab size are not obviously relevant, but engine specifications seem to be. Are these specifications numerous enough? That depends on whether anything else that we are not aware of contributes to overall reliability. Of course, if the trucks having the features we know also have all other relevant features we do not know (if there are any), then Buyer 2 may still be able to draw a reliable inference from analogy. Of course, we do not currently know this.

Alternatively, Buyer 1 seems to have very few relevant features on which to draw a reliable analogy. The features listed are not obviously related to safety. Are there safety options a buyer may choose but that are not included in the list? For example, can a buyer choose side-curtain airbags, or do such airbags come standard in this model? Does cab size contribute to overall safety? Although there are a number of similarities between the trucks, it is not obvious that we have identified features relevant to safety or whether there are enough of them. Further, reports of “feeling safe” are not equivalent to a truck actually being safe. Better evidence would be crash test data or data from actual accidents involving this truck. This information is not likely to be on a consumer reports website.

A further difficulty is that, in many cases, it is difficult to know whether many similarities are necessary if the similarities are relevant. For instance, if having lots of room for passengers is your primary concern, then any other features are relevant only insofar as they affect cab size. The features that affect cab size may be relatively small.

This example shows that arguments from analogy are difficult to formulate well. Arguments from analogy can be good arguments when critical thinkers identify a sufficient number of features of known objects that are also relevant to the feature inferred to be shared by the object in question. If a rock is shaped like a cutting tool, has marks consistent with shaping and sharpening, and has wear marks consistent with being held in a human hand, it is likely that rock is an artifact. But not all cases are as clear.

It is often difficult to determine whether the features we have identified are sufficiently numerous or relevant to our interests. To determine whether an argument from analogy is good, a person may need to identify a causal relationship between those features and the one in which she is interested (as in the case with a vehicle’s mechanical reliability). This usually takes the form of an experiment, which we explore below (§4c).

Difficulties with constructing reliable generalizations and analogies have led critical thinkers to develop sophisticated methods for controlling for the ways these arguments can go wrong. The most common way to avoid the pitfalls of these arguments is to identify the causal structures in the world that account for or underwrite successful generalizations and analogies. Causal arguments are the primary method of controlling for extraneous causal influences and identifying relevant causes. Their development and complexity warrant regarding them as a distinct form of informal reasoning.

c. Causal Reasoning

Causal arguments attempt to draw causal conclusions (that is, statements that express propositions about causes: x causes y ) from premises about relationships among events or objects. Though it is not always possible to construct a causal argument, when available, they have an advantage over other types of inductive arguments in that they can employ mechanisms (experiments) that reduce the risks involved in generalizations and analogies.

The interest in identifying causal relationships often begins with the desire to explain correlations among events (as pollen levels increase, so do allergy symptoms) or with the desire to replicate an event (building muscle, starting a fire) or to eliminate an event (polio, head trauma in football).

Correlations among events may be positive (where each event increases at roughly the same rate) or negative (where one event decreases in proportion to another’s increase). Correlations suggest a causal relationship among the events correlated.

But we must be careful; correlations are merely suggestive—other forces may be at work. Let us say the y-axis in the charts above represents the number of millionaires in the U.S. and the x-axis represents the amount of money U.S. citizens pay for healthcare each year. Without further analysis, a positive correlation between these two may lead someone to conclude that increasing wealth causes people to be more health conscious and to seek medical treatment more often. A negative correlation may lead someone to conclude that wealth makes people healthier and, therefore, that they need to seek medical care less frequently.

Unfortunately, correlations can occur without any causal structures (mere coincidence) or because of a third, as-yet-unidentified event (a cause common to both events, or “common cause”), or the causal relationship may flow in an unexpected direction (what seems like the cause is really the effect). In order to determine precisely which event (if any) is responsible for the correlation, reasoners must eliminate possible influences on the correlation by “controlling” for possible influences on the relationship (variables).

Critical thinking about causes begins by constructing hypotheses about the origins of particular events. A hypothesis is an explanation or event that would account for the event in question. For example, if the question is how to account for increased acne during adolescence, and we are not aware of the existence of hormones, we might formulate a number of hypotheses about why this happens: during adolescence, people’s diets change (parents no longer dictate their meals), so perhaps some types of food cause acne; during adolescence, people become increasingly anxious about how they appear to others, so perhaps anxiety or stress causes acne; and so on.

After we have formulated a hypothesis, we identify a test implication that will help us determine whether our hypothesis is correct. For instance, if some types of food cause acne, we might choose a particular food, say, chocolate, and say: if chocolate causes acne (hypothesis), then decreasing chocolate will decrease acne (test implication). We then conduct an experiment to see whether our test implication occurs.

Reasoning about our experiment would then look like one of the following arguments:

There are a couple of important things to note about these arguments. First, despite appearances, both are inductive arguments. The one on the left commits the formal fallacy of affirming the consequent, so, at best, the premises confer only some degree of probability on the conclusion. The argument on the right looks to be deductive (on the face of it, it has the valid form modus tollens ), but it would be inappropriate to regard it deductively. This is because we are not evaluating a logical connection between H and TI, we are evaluating a causal connection—TI might be true or false regardless of H (we might have chosen an inappropriate test implication or simply gotten lucky), and therefore, we cannot conclude with certainty that H does not causally influence TI. Therefore, “If…, then…” statements in experiments must be read as causal conditionals and not material conditionals (the term for how we used conditionals above).

Second, experiments can go wrong in many ways, so no single experiment will grant a high degree of probability to its causal conclusion. Experiments may be biased by hidden variables (causes we did not consider or detect, such as age, diet, medical history, or lifestyle), auxiliary assumptions (the theoretical assumptions by which evaluating the results may be faulty), or underdetermination (there may be a number of hypotheses consistent with those results; for example, if it is actually sugar that causes acne, then chocolate bars, ice cream, candy, and sodas would yield the same test results). Because of this, experiments either confirm or disconfirm a hypothesis; that is, they give us some reason (but not a particularly strong reason) to believe our hypothesized causes are or are not the causes of our test implications, and therefore, of our observations (see Quine and Ullian, 1978). Because of this, experiments must be conducted many times, and only after we have a number of confirming or disconfirming results can we draw a strong inductive conclusion. (For more, see “ Confirmation and Induction .”)

Experiments may be formal or informal . In formal experiments, critical thinkers exert explicit control over experimental conditions: experimenters choose participants, include or exclude certain variables, and identify or introduce hypothesized events. Test subjects are selected according to control criteria (criteria that may affect the results and, therefore, that we want to mitigate, such as age, diet, and lifestyle) and divided into control groups (groups where the hypothesized cause is absent) and experimental groups (groups where the hypothesized cause is present, either because it is introduced or selected for).

Subjects are then placed in experimental conditions. For instance, in a randomized study, the control group receives a placebo (an inert medium) whereas the experimental group receives the hypothesized cause—the putative cause is introduced, the groups are observed, and the results are recorded and compared. When a hypothesized cause is dangerous (such as smoking) or its effects potentially irreversible (for instance, post-traumatic stress disorder), the experimental design must be restricted to selecting for the hypothesized cause already present in subjects, for example, in retrospective (backward-looking) and prospective (forward-looking) studies. In all types of formal experiments, subjects are observed under exposure to the test or placebo conditions for a specified time, and results are recorded and compared.

In informal experiments, critical thinkers do not have access to sophisticated equipment or facilities and, therefore, cannot exert explicit control over experimental conditions. They are left to make considered judgments about variables. The most common informal experiments are John Stuart Mill’s five methods of inductive reasoning, called Mill’s Methods, which he first formulated in A System of Logic (1843). Here is a very brief summary of Mill’s five methods:

(1) The Method of Agreement

If all conditions containing the event y also contain x , x is probably the cause of y .

For example:

“I’ve eaten from the same box of cereal every day this week, but all the times I got sick after eating cereal were times when I added strawberries. Therefore, the strawberries must be bad.”

(2) The Method of Difference

If all conditions lacking y also lack x , x is probably the cause of y .

“The organization turned all its tax forms in on time for years, that is, until our comptroller, George, left; after that, we were always late. Only after George left were we late. Therefore, George was probably responsible for getting our tax forms in on time.”

(3) The Joint Method of Agreement and Difference

If all conditions containing event y also contain event x , and all events lacking y also lack x , x is probably the cause of y .

“The conditions at the animal shelter have been pretty regular, except we had a string of about four months last year when the dogs barked all night, every night. But at the beginning of those four months we sheltered a redbone coonhound, and the barking stopped right after a family adopted her. All the times the redbone hound wasn’t present, there was no barking. Only the time she was present was there barking. Therefore, she probably incited all the other dogs to bark.”

(4) The Method of Concomitant Variation

If the frequency of event y increases and decreases as event x increases and decreases, respectively, x is probably the cause of y .

“We can predict the amount of alcohol sales by the rate of unemployment. As unemployment rises, so do alcohol sales. As unemployment drops, so do alcohol sales. Last quarter marked the highest unemployment in three years, and our sales last quarter are the highest they had been in those three years. Therefore, unemployment probably causes people to buy alcohol.”

(5) The Method of Residues

If a number of factors x , y , and z , may be responsible for a set of events A , B , and C , and if we discover reasons for thinking that x is the cause of A and y is the cause of B , then we have reason to believe z is the cause of C .

“The people who come through this medical facility are usually starving and have malaria, and a few have polio. We are particularly interested in treating the polio. Take this patient here: she is emaciated, which is caused by starvation; and she has a fever, which is caused by malaria. But notice that her muscles are deteriorating, and her bones are sore. This suggests she also has polio.”

d. Abduction

Not all inductive reasoning is inferential. In some cases, an explanation is needed before we can even begin drawing inferences. Consider Darwin’s idea of natural selection. Natural selection is not an object, like a blood vessel or a cellular wall, and it is not, strictly speaking, a single event. It cannot be detected in individual organisms or observed in a generation of offspring. Natural selection is an explanation of biodiversity that combines the process of heritable variation and environmental pressures to account for biomorphic change over long periods of time. With this explanation in hand, we can begin to draw some inferences. For instance, we can separate members of a single species of fruit flies, allow them to reproduce for several generations, and then observe whether the offspring of the two groups can reproduce. If we discover they cannot reproduce, this is likely due to certain mutations in their body types that prevent them from procreating. And since this is something we would expect if natural selection were true, we have one piece of confirming evidence for natural selection. But how do we know the explanations we come up with are worth our time?

Coined by C. S. Peirce (1839-1914), abduction , also called retroduction, or inference to the best explanation , refers to a way of reasoning informally that provides guidelines for evaluating explanations. Rather than appealing to types of arguments (generalization, analogy, causation), the value of an explanation depends on the theoretical virtues it exemplifies. A theoretical virtue is a quality that renders an explanation more or less fitting as an account of some event. What constitutes fittingness (or “loveliness,” as Peter Lipton (2004) calls it) is controversial, but many of the virtues are intuitively compelling, and abduction is a widely accepted tool of critical thinking.

The most widely recognized theoretical virtue is probably simplicity , historically associated with William of Ockham (1288-1347) and known as Ockham’s Razor . A legend has it that Ockham was asked whether his arguments for God’s existence prove that only one God exists or whether they allow for the possibility that many gods exist. He supposedly responded, “Do not multiply entities beyond necessity.” Though this claim is not found in his writings, Ockham is now famous for advocating that we restrict our beliefs about what is true to only what is absolutely necessary for explaining what we observe.

In contemporary theoretical use, the virtue of simplicity is invoked to encourage caution in how many mechanisms we introduce to explain an event. For example, if natural selection can explain the origin of biological diversity by itself, there is no need to hypothesize both natural selection and a divine designer. But if natural selection cannot explain the origin of, say, the duck-billed platypus, then some other mechanism must be introduced. Of course, not just any mechanism will do. It would not suffice to say the duck-billed platypus is explained by natural selection plus gremlins. Just why this is the case depends on other theoretical virtues; ideally, the virtues work together to help critical thinkers decide among competing hypotheses to test. Here is a brief sketch of some other theoretical virtues or ideals:

Conservatism – a good explanation does not contradict well-established views in a field.

Independent Testability – a good explanation is successful on different occasions under similar circumstances.

Fecundity – a good explanation leads to results that make even more research possible.

Explanatory Depth – a good explanation provides details of how an event occurs.

Explanatory Breadth – a good explanation also explains other, similar events.

Though abduction is structurally distinct from other inductive arguments, it functions similarly in practice: a good explanation provides a probabilistic reason to believe a proposition. This is why it is included here as a species of inductive reasoning. It might be thought that explanations only function to help critical thinkers formulate hypotheses, and do not, strictly speaking, support propositions. But there are intuitive examples of explanations that support propositions independently of however else they may be used. For example, a critical thinker may argue that material objects exist outside our minds is a better explanation of why we perceive what we do (and therefore, a reason to believe it) than that an evil demon is deceiving me , even if there is no inductive or deductive argument sufficient for believing that the latter is false. (For more, see “ Charles Sanders Peirce: Logic .”)

5. Detecting Poor Reasoning

Our attempts at thinking critically often go wrong, whether we are formulating our own arguments or evaluating the arguments of others. Sometimes it is in our interests for our reasoning to go wrong, such as when we would prefer someone to agree with us than to discover the truth value of a proposition. Other times it is not in our interests; we are genuinely interested in the truth, but we have unwittingly made a mistake in inferring one proposition from others. Whether our errors in reasoning are intentional or unintentional, such errors are called fallacies (from the Latin, fallax, which means “deceptive”). Recognizing and avoiding fallacies helps prevent critical thinkers from forming or maintaining defective beliefs.

Fallacies occur in a number of ways. An argument’s form may seem to us valid when it is not, resulting in a formal fallacy . Alternatively, an argument’s premises may seem to support its conclusion strongly but, due to some subtlety of meaning, do not, resulting in an informal fallacy . Additionally, some of our errors may be due to unconscious reasoning processes that may have been helpful in our evolutionary history, but do not function reliably in higher order reasoning. These unconscious reasoning processes are now widely known as heuristics and biases . Each type is briefly explained below.

a. Formal Fallacies

Formal fallacies occur when the form of an argument is presumed or seems to be valid (whether intentionally or unintentionally) when it is not. Formal fallacies are usually invalid variations of valid argument forms. Consider, for example, the valid argument form modus ponens (this is one of the rules of inference mentioned in §3b):

modus ponens (valid argument form)

In modus ponens , we assume or “affirm” both the conditional and the left half of the conditional (called the antecedent ): (p à q) and p. From these, we can infer that q, the second half or consequent , is true. This a valid argument form: if the premises are true, the conclusion cannot be false.

Sometimes, however, we invert the conclusion and the second premise, affirming that the conditional, (p à q), and the right half of the conditional, q (the consequent), are true, and then inferring that the left half, p (the antecedent), is true. Note in the example below how the conclusion and second premise are switched. Switching them in this way creates a problem.

To get an intuitive sense of why “affirming the consequent” is a problem, consider this simple example:

affirming the consequent

• It is a mammal.
• Therefore, it is a cat.(?)

From the fact that something is a mammal, we cannot conclude that it is a cat. It may be a dog or a mouse or a whale. The premises can be true and yet the conclusion can still be false. Therefore, this is not a valid argument form. But since it is an easy mistake to make, it is included in the set of common formal fallacies.

Here is a second example with the rule of inference called modus tollens . Modus tollens involves affirming a conditional, (p à q), and denying that conditional’s consequent: ~q. From these two premises, we can validly infer the denial of the antecedent: ~p. But if we switch the conclusion and the second premise, we get another fallacy, called denying the antecedent .

Technically, all informal reasoning is formally fallacious—all informal arguments are invalid. Nevertheless, since those who offer inductive arguments rarely presume they are valid, we do not regard them as reasoning fallaciously.

b. Informal Fallacies

Informal fallacies occur when the meaning of the terms used in the premises of an argument suggest a conclusion that does not actually follow from them (the conclusion either follows weakly or with no strength at all). Consider an example of the informal fallacy of equivocation , in which a word with two distinct meanings is used in both of its meanings:

• Any law can be repealed by Congress.
• Gravity is a law.
• Therefore, gravity can be repealed by Congress.

In this case, the argument’s premises are true when the word “law” is rightly interpreted, but the conclusion does not follow because the word law has a different referent in premise 1 (political laws) than in premise 2 (a law of nature). This argument equivocates on the meaning of law and is, therefore, fallacious.

Consider, also, the informal fallacy of ad hominem , abusive, when an arguer appeals to a person’s character as a reason to reject her proposition:

“Elizabeth argues that humans do not have souls; they are simply material beings. But Elizabeth is a terrible person and often talks down to children and the elderly. Therefore, she could not be right that humans do not have souls.”

The argument might look like this:

• Elizabeth is a terrible person and often talks down to children and the elderly.
• Therefore, Elizabeth is not right that humans do not have souls.

The conclusion does not follow because whether Elizabeth is a terrible person is irrelevant to the truth of the proposition that humans do not have souls. Elizabeth’s argument for this statement is relevant, but her character is not.

Another way to evaluate this fallacy is to note that, as the argument stands, it is an enthymeme (see §2); it is missing a crucial premise, namely: If anyone is a terrible person, that person makes false statements. But this premise is clearly false. There are many ways in which one can be a terrible person, and not all of them imply that someone makes false statements. (In fact, someone could be terrible precisely because they are viciously honest.) Once we fill in the missing premise, we see the argument is not cogent because at least one premise is false.

Importantly, we face a number of informal fallacies on a daily basis, and without the ability to recognize them, their regularity can make them seem legitimate. Here are three others that only scratch the surface:

Appeal to the People: We are often encouraged to believe or do something just because everyone else does. We are encouraged to believe what our political party believes, what the people in our churches or synagogues or mosques believe, what people in our family believe, and so on. We are encouraged to buy things because they are “bestsellers” (lots of people buy them). But the fact that lots of people believe or do something is not, on its own, a reason to believe or do what they do.

Tu Quoque (You, too!): We are often discouraged from pursuing a conclusion or action if our own beliefs or actions are inconsistent with them. For instance, if someone attempts to argue that everyone should stop smoking, but that person smokes, their argument is often given less weight: “Well, you smoke! Why should everyone else quit?” But the fact that someone believes or does something inconsistent with what they advocate does not, by itself, discredit the argument. Hypocrites may have very strong arguments despite their personal inconsistencies.

Base Rate Neglect: It is easy to look at what happens after we do something or enact a policy and conclude that the act or policy caused those effects. Consider a law reducing speed limits from 75 mph to 55 mph in order to reduce highway accidents. And, in fact, in the three years after the reduction, highway accidents dropped 30%! This seems like a direct effect of the reduction. However, this is not the whole story. Imagine you looked back at the three years prior to the law and discovered that accidents had dropped 30% over that time, too. If that happened, it might not actually be the law that caused the reduction in accidents. The law did not change the trend in accident reduction. If we only look at the evidence after the law, we are neglecting the rate at which the event occurred without the law. The base rate of an event is the rate that the event occurs without the potential cause under consideration. To take another example, imagine you start taking cold medicine, and your cold goes away in a week. Did the cold medicine cause your cold to go away? That depends on how long colds normally last and when you took the medicine. In order to determine whether a potential cause had the effect you suspect, do not neglect to compare its putative effects with the effects observed without that cause.

For more on formal and informal fallacies and over 200 different types with examples, see “ Fallacies .”

c. Heuristics and Biases

In the 1960s, psychologists began to suspect there is more to human reasoning than conscious inference. Daniel Kahneman and Amos Tversky confirmed these suspicions with their discoveries that many of the standard assumptions about how humans reason in practice are unjustified. In fact, humans regularly violate these standard assumptions, the most significant for philosophers and economists being that humans are fairly good at calculating the costs and benefits of their behavior; that is, they naturally reason according to the dictates of Expected Utility Theory. Kahneman and Tversky showed that, in practice, reasoning is affected by many non-rational influences, such as the wording used to frame scenarios (framing bias) and information most vividly available to them (the availability heuristic).

Consider the difference in your belief about the likelihood of getting robbed before and after seeing a news report about a recent robbery, or the difference in your belief about whether you will be bitten by a shark the week before and after Discovery Channel’s “Shark Week.” For most of us, we are likely to regard their likelihood as higher after we have seen these things on television than before. Objectively, they are no more or less likely to happen regardless of our seeing them on television, but we perceive they are more likely because their possibility is more vivid to us. These are examples of the availability heuristic.

Since the 1960s, experimental psychologists and economists have conducted extensive research revealing dozens of these unconscious reasoning processes, including ordering bias , the representativeness heuristic , confirmation bias , attentional bias , and the anchoring effect . The field of behavioral economics, made popular by Dan Ariely (2008; 2010; 2012) and Richard Thaler and Cass Sunstein (2009), emerged from and contributes to heuristics and biases research and applies its insights to social and economic behaviors.

Ideally, recognizing and understanding these unconscious, non-rational reasoning processes will help us mitigate their undermining influence on our reasoning abilities (Gigerenzer, 2003). However, it is unclear whether we can simply choose to overcome them or whether we have to construct mechanisms that mitigate their influence (for instance, using double-blind experiments to prevent confirmation bias).

6. The Scope and Virtues of Good Reasoning

Whether the process of critical thinking is productive for reasoners—that is, whether it actually answers the questions they are interested in answering—often depends on a number of linguistic, psychological, and social factors. We encountered some of the linguistic factors in §1. In closing, let us consider some of the psychological and social factors that affect the success of applying the tools of critical thinking.

Not all psychological and social contexts are conducive for effective critical thinking. When reasoners are depressed or sad or otherwise emotionally overwhelmed, critical thinking can often be unproductive or counterproductive. For instance, if someone’s child has just died, it would be unproductive (not to mention cruel) to press the philosophical question of why a good God would permit innocents to suffer or whether the child might possibly have a soul that could persist beyond death. Other instances need not be so extreme to make the same point: your company’s holiday party (where most people would rather remain cordial and superficial) is probably not the most productive context in which to debate the president’s domestic policy or the morality of abortion.

The process of critical thinking is primarily about detecting truth, and truth may not always be of paramount value. In some cases, comfort or usefulness may take precedence over truth. The case of the loss of a child is a case where comfort seems to take precedence over truth. Similarly, consider the case of determining what the speed limit should be on interstate highways. Imagine we are trying to decide whether it is better to allow drivers to travel at 75 mph or to restrict them to 65. To be sure, there may be no fact of the matter as to which is morally better, and there may not be any difference in the rate of interstate deaths between states that set the limit at 65 and those that set it at 75. But given the nature of the law, a decision about which speed limit to set must be made. If there is no relevant difference between setting the limit at 65 and setting it at 75, critical thinking can only tell us that , not which speed limit to set. This shows that, in some cases, concern with truth gives way to practical or preferential concerns (for example, Should I make this decision on the basis of what will make citizens happy? Should I base it on whether I will receive more campaign contributions from the business community?). All of this suggests that critical thinking is most productive in contexts where participants are already interested in truth.

b. The Principle of Charity/Humility

Critical thinking is also most productive when people in the conversation regard themselves as fallible, subject to error, misinformation, and deception. The desire to be “right” has a powerful influence on our reasoning behavior. It is so strong that our minds bias us in favor of the beliefs we already hold even in the face of disconfirming evidence (a phenomenon known as “confirmation bias”). In his famous article, “The Ethics of Belief” (1878), W. K. Clifford notes that, “We feel much happier and more secure when we think we know precisely what to do, no matter what happens, than when we have lost our way and do not know where to turn. … It is the sense of power attached to a sense of knowing that makes men desirous of believing, and afraid of doubting” (2010: 354).

Nevertheless, when we are open to the possibility that we are wrong, that is, if we are humble about our conclusions and we interpret others charitably, we have a better chance at having rational beliefs in two senses. First, if we are genuinely willing to consider evidence that we are wrong—and we demonstrate that humility—then we are more likely to listen to others when they raise arguments against our beliefs. If we are certain we are right, there would be little reason to consider contrary evidence. But if we are willing to hear it, we may discover that we really are wrong and give up faulty beliefs for more reasonable ones.

Second, if we are willing to be charitable to arguments against our beliefs, then if our beliefs are unreasonable, we have an opportunity to see the ways in which they are unreasonable. On the other hand, if our beliefs are reasonable, then we can explain more effectively just how well they stand against the criticism. This is weakly analogous to competition in certain types of sporting events, such as basketball. If you only play teams that are far inferior to your own, you do not know how good your team really is. But if you can beat a well-respected team on fair terms, any confidence you have is justified.

c. The Principle of Caution

In our excitement over good arguments, it is easy to overextend our conclusions, that is, to infer statements that are not really warranted by our evidence. From an argument for a first, uncaused cause of the universe, it is tempting to infer the existence of a sophisticated deity such as that of the Judeo-Christian tradition. From an argument for the compatibilism of the free will necessary for moral responsibility and determinism, it is tempting to infer that we are actually morally responsible for our behaviors. From an argument for negative natural rights, it is tempting to infer that no violation of a natural right is justifiable. Therefore, it is prudent to continually check our conclusions to be sure they do not include more content than our premises allow us to infer.

Of course, the principle of caution must itself be used with caution. If applied too strictly, it may lead reasoners to suspend all belief, and refrain from interacting with one another and their world. This is not, strictly speaking, problematic; ancient skeptics, such as the Pyrrhonians, advocated suspending all judgments except those about appearances in hopes of experiencing tranquility. However, at least some judgments about the long-term benefits and harms seem indispensable even for tranquility, for instance, whether we should retaliate in self-defense against an attacker or whether we should try to help a loved one who is addicted to drugs or alcohol.

d. The Expansiveness of Critical Thinking

The importance of critical thinking cannot be overstated because its relevance extends into every area of life, from politics, to science, to religion, to ethics. Not only does critical thinking help us draw inferences for ourselves, it helps us identify and evaluate the assumptions behind statements, the moral implications of statements, and the ideologies to which some statements commit us. This can be a disquieting and difficult process because it forces us to wrestle with preconceptions that might not be accurate. Nevertheless, if the process is conducted well, it can open new opportunities for dialogue, sometimes called “critical spaces,” that allow people who might otherwise disagree to find beliefs in common from which to engage in a more productive conversation.

It is this possibility of creating critical spaces that allows philosophical approaches like Critical Theory to effectively challenge the way social, political, and philosophical debates are framed. For example, if a discussion about race or gender or sexuality or gender is framed in terms that, because of the origins those terms or the way they have functioned socially, alienate or disproportionately exclude certain members of the population, then critical space is necessary for being able to evaluate that framing so that a more productive dialogue can occur (see Foresman, Fosl, and Watson, 2010, ch. 10 for more on how critical thinking and Critical Theory can be mutually supportive).

e. Productivity and the Limits of Rationality

Despite the fact that critical thinking extends into every area of life, not every important aspect of our lives is easily or productively subjected to the tools of language and logic. Thinkers who are tempted to subject everything to the cold light of reason may discover they miss some of what is deeply enjoyable about living. The psychologist Abraham Maslow writes, “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail” (1966: 16). But it is helpful to remember that language and logic are tools, not the projects themselves. Even formal reasoning systems depend on axioms that are not provable within their own systems (consider Euclidean geometry or Peano arithmetic). We must make some decisions about what beliefs to accept and how to live our lives on the basis of considerations outside of critical thinking.

Borrowing an example from William James (1896), consider the statement, “Religion X is true.” James says that, while some people find this statement interesting, and therefore, worth thinking critically about, others may not be able to consider the truth of the statement. For any particular religious tradition, we might not know enough about it to form a belief one way or the other, and even suspending judgment may be difficult, since it is not obvious what we are suspending judgment about.

If I say to you: ‘Be a theosophist or be a Mohammedan,’ it is probably a dead option, because for you neither hypothesis is likely to be alive. But if I say: ‘Be an agnostic or be a Christian,’ it is otherwise: trained as you are, each hypothesis makes some appeal, however small, to your belief (2010: 357).

Ignoring the circularity in his definition of “dead option,” James’s point seems to be that if you know nothing about a view or what statements it entails, no amount of logic or evidence could help you form a reasonable belief about that position.

We might criticize James at this point because his conclusion seems to imply that we have no duty to investigate dead options, that is, to discover if there is anything worth considering in them. If we are concerned with truth, the simple fact that we are not familiar with a proposition does not mean it is not true or potentially significant for us. But James’s argument is subtler than this criticism suggests. Even if you came to learn about a particularly foreign religious tradition, its tenets may be so contrary to your understanding of the world that you could not entertain them as possible beliefs of yours . For instance, you know perfectly well that, if some events had been different, Hitler would not have existed: his parents might have had no children, or his parents’ parents might have had no children. You know roughly what it would mean for Hitler not to have existed and the sort of events that could have made it true that he did not exist. But how much evidence would it take to convince you that, in fact, Hitler did not exist, that is, that your belief that Hitler did exist is false ? Could there be an argument strong enough? Not obviously. Since all the information we have about Hitler unequivocally points to his existence, any arguments against that belief would have to affect a very broad range of statements; they would have to be strong enough to make us skeptical of large parts of reality.

7. Approaches to Improving Reasoning through Critical Thinking

Recall that the goal of critical thinking is not just to study what makes reasons and statements good, but to help us improve our ability to reason, that is, to improve our ability to form, hold, and discard beliefs according to whether they meet the standards of good thinking. Some ways of approaching this latter goal are more effective than others. While the classical approach focuses on technical reasoning skills, the Paul/Elder model encourages us to think in terms of critical concepts, and irrationality approaches use empirical research on instances of poor reasoning to help us improve reasoning where it is least obvious we need it and where we need it most. Which approach or combination of approaches is most effective depends, as noted above, on the context and limits of critical thinking, but also on scientific evidence of their effectiveness. Those who teach critical thinking, of all people, should be engaged with the evidence relevant to determining which approaches are most effective.

a. Classical Approaches

The classic approach to critical thinking follows roughly the structure of this article: critical thinkers attempt to interpret statements or arguments clearly and charitably, and then they apply the tools of formal and informal logic and science, while carefully attempting to avoid fallacious inferences (see Weston, 2008; Walton, 2008; Watson and Arp, 2015). This approach requires spending extensive time learning and practicing technical reasoning strategies. It presupposes that reasoning is primarily a conscious activity, and that enhancing our skills in these areas will improve our ability to reason well in ordinary situations.

There are at least two concerns about this approach. First, it is highly time intensive relative to its payoff. Learning the terminology of systems like propositional and categorical logic and the names of the fallacies, and practicing applying these tools to hypothetical cases requires significant time and energy. And it is not obvious, given the problems with heuristics and biases, whether this practice alone makes us better reasoners in ordinary contexts. Second, many of the ways we reason poorly are not consciously accessible (recall the heuristics and biases discussion in §5c). Our biases, combined with the heuristics we rely on in ordinary situations, can only be detected in experimental settings, and addressing them requires restructuring the ways in which we engage with evidence (see Thaler and Sunstein, 2009).

b. The Paul/Elder Model

Richard Paul and Linda Elder (Paul and Elder, 2006; Paul, 2012) developed an alternative to the classical approach on the assumption that critical thinking is not something that is limited to academic study or to the discipline of philosophy. On their account, critical thinking is a broad set of conceptual skills and habits aimed at a set of standards that are widely regarded as virtues of thinking: clarity, accuracy, depth, fairness, and others. They define it simply as “the art of analyzing and evaluating thinking with a view to improving it” (2006: 4). Their approach, then, is to focus on the elements of thought and intellectual virtues that help us form beliefs that meet these standards.

The Paul/Elder model is made up of three sets of concepts: elements of thought, intellectual standards, and intellectual traits. In this model, we begin by identifying the features present in every act of thought. They use “thought” to mean critical thought aimed at forming beliefs, not just any act of thinking, musing, wishing, hoping, remembering. According to the model, every act of thought involves:

These comprise the subject matter of critical thinking; that is, they are what we are evaluating when we are thinking critically. We then engage with this subject matter by subjecting them to what Paul and Elder call universal intellectual standards. These are evaluative goals we should be aiming at with our thinking:

While in classical approaches, logic is the predominant means of thinking critically, in the Paul/Elder model, it is put on equal footing with eight other standards. Finally, Paul and Elder argue that it is helpful to approach the critical thinking process with a set of intellectual traits or virtues that dispose us to using elements and standards well.

To remind us that these are virtues of thought relevant to critical thinking, they use “intellectual” to distinguish these traits from their moral counterparts (moral integrity, moral courage, and so on).

The aim is that, as we become familiar with these three sets of concepts and apply them in everyday contexts, we become better at analyzing and evaluating statements and arguments in ordinary situations.

Like the classical approach, this approach presupposes that reasoning is primarily a conscious activity, and that enhancing our skills will improve our reasoning. This means that it still lacks the ability to address the empirical evidence that many of our reasoning errors cannot be consciously detected or corrected. It differs from the classical approach in that it gives the technical tools of logic a much less prominent role and places emphasis on a broader, and perhaps more intuitive, set of conceptual tools. Learning and learning to apply these concepts still requires a great deal of time and energy, though perhaps less than learning formal and informal logic. And these concepts are easy to translate into disciplines outside philosophy. Students of history, psychology, and economics can more easily recognize the relevance of asking questions about an author’s point of view and assumptions than perhaps determining whether the author is making a deductive or inductive argument. The question, then, is whether this approach improves our ability to think better than the classical approach.

c. Other Approaches

A third approach that is becoming popular is to focus on the ways we commonly reason poorly and then attempt to correct them. This can be called the Rationality Approach , and it takes seriously the empirical evidence (§5c) that many of our errors in reasoning are not due to a lack of conscious competence with technical skills or misusing those skills, but are due to subconscious dispositions to ignore or dismiss relevant information or to rely on irrelevant information.

One way to pursue this approach is to focus on beliefs that are statistically rare or “weird.” These include beliefs of fringe groups, such as conspiracy theorists, religious extremists, paranormal psychologists, and proponents of New Age metaphysics (see Gilovich, 1992; Vaughn and Schick, 2010; Coady, 2012). If we recognize the sorts of tendencies that lead to these controversial beliefs, we might be able to recognize and avoid similar tendencies in our own reasoning about less extreme beliefs, such as beliefs about financial investing, how statistics are used to justify business decisions, and beliefs about which public policies to vote for.

Another way to pursue this approach is to focus directly on the research on error, those ordinary beliefs that psychologists and behavioral economists have discovered we reason poorly, and to explore ways of changing how we frame decisions about what to believe (see Nisbett and Ross, 1980; Gilovich, 1992; Ariely, 2008; Kahneman, 2011). For example, in one study, psychologists found that judges issue more convictions just before lunch and the end of the day than in the morning or just after lunch (Danzinger, et al., 2010). Given that dockets do not typically organize cases from less significant crimes to more significant crimes, this evidence suggests that something as irrelevant as hunger can bias judicial decisions. Even though hunger has nothing to do with the truth of a belief, knowing that it can affect how we evaluate a belief can help us avoid that effect. This study might suggest something as simple as that we should avoid being hungry when making important decisions. The more we learn ways in which our brains use irrelevant information, the better we can organize our reasoning to avoid these mistakes. For more on how decisions can be improved by restructuring our decisions, see Thaler and Sunstein, 2009.

A fourth approach is to take more seriously the role that language plays in our reasoning. Arguments involve complex patterns of expression, and we have already seen how vagueness and ambiguity can undermine good reasoning (§1). The pragma-dialectics approach (or pragma-dialectical theory) is the view that the quality of an argument is not solely or even primarily a matter of its logical structure, but is more fundamentally a matter of whether it is a form of reasonable discourse (Van Eemeren and Grootendorst, 1992). The proponents of this view contend that, “The study of argumentation should … be construed as a special branch of linguistic pragmatics in which descriptive and normative perspectives on argumentative discourse are methodically integrated” (Van Eemeren and Grootendorst, 1995: 130).

The pragma-dialectics approach is a highly technical approach that uses insights from speech act theory, H. P. Grice’s philosophy of language, and the study of discourse analysis. Its use, therefore, requires a great deal of background in philosophy and linguistics. It has an advantage over other approaches in that it highlights social and practical dimensions of arguments that other approaches largely ignore. For example, argument is often public ( external ), in that it creates an opportunity for opposition, which influences people’s motives and psychological attitudes toward their arguments. Argument is also social in that it is part of a discourse in which two or more people try to arrive at an agreement. Argument is also functional ; it aims at a resolution that can only be accommodated by addressing all the aspects of disagreement or anticipated disagreement, which can include public and social elements. Argument also has a rhetorical role ( dialectical ) in that it is aimed at actually convincing others, which may have different requirements than simply identifying the conditions under which they should be convinced.

These four approaches are not mutually exclusive. All of them presuppose, for example, the importance of inductive reasoning and scientific evidence. Their distinctions turn largely on which aspects of statements and arguments should take precedence in the critical thinking process and on what information will help us have better beliefs.

8. References and Further Reading

• Ariely, Dan. 2008. Predictably Irrational: The Hidden Forces that Shape Our Decisions. New York: Harper Perennial.
• Ariely, Dan. 2010. The Upside of Irrationality. New York: Harper Perennial.
• Ariely, Dan. 2012. The (Honest) Truth about Dishonesty. New York: Harper Perennial.
• Aristotle. 2002. Categories and De Interpretatione, J. L. Akrill, editor. Oxford: University of Oxford Press.
• Clifford, W. K. 2010. “The Ethics of Belief.” In Nils Ch. Rauhut and Robert Bass, eds., Readings on the Ultimate Questions: An Introduction to Philosophy, 3rd ed. Boston: Prentice Hall, 351-356.
• Chomsky, Noam. 1957/2002. Syntactic Structures. Berlin: Mouton de Gruyter.
• Coady, David. What To Believe Now: Applying Epistemology to Contemporary Issues. Malden, MA: Wiley-Blackwell, 2012.
• Danzinger, Shai, Jonathan Levav, and Liora Avnaim-Pesso. 2011. “Extraneous Factors in Judicial Decisions.” Proceedings of the National Academy of Sciences of the United States of America. Vol. 108, No. 17, 6889-6892. doi: 10.1073/pnas.1018033108.
• Foresman, Galen, Peter Fosl, and Jamie Carlin Watson. 2017. The Critical Thinking Toolkit. Malden, MA: Wiley-Blackwell.
• Fogelin, Robert J. and Walter Sinnott-Armstrong. 2009. Understanding Arguments: An Introduction to Informal Logic, 8th ed. Belmont, CA: Wadsworth Cengage Learning.
• Gigerenzer, Gerd. 2003. Calculated Risks: How To Know When Numbers Deceive You. New York: Simon and Schuster.
• Gigerenzer, Gerd, Peter Todd, and the ABC Research Group. 2000. Simple Heuristics that Make Us Smart. Oxford University Press.
• Gilovich, Thomas. 1992. How We Know What Isn’t So. New York: Free Press.
• James, William. “The Will to Believe”, in Nils Ch. Rauhut and Robert Bass, eds., Readings on the Ultimate Questions: An Introduction to Philosophy, 3rd ed. Boston: Prentice Hall, 2010, 356-364.
• Kahneman, Daniel. 2011. Thinking Fast and Slow. New York: Farrar, Strauss and Giroux.
• Lewis, David. 1986. On the Plurality of Worlds. Oxford Blackwell.
• Lipton, Peter. 2004. Inference to the Best Explanation, 2nd ed. London: Routledge.
• Maslow, Abraham. 1966. The Psychology of Science: A Reconnaissance. New York: Harper & Row.
• Mill, John Stuart. 2011. A System of Logic, Ratiocinative and Inductive. New York: Cambridge University Press.
• Nisbett, Richard and Lee Ross. 1980. Human Inference: Strategies and Shortcomings of Social Judgment. Englewood Cliffs, NJ: Prentice Hall.
• Paul, Richard. 2012. Critical Thinking: What Every Person Needs to Survive in a Rapidly Changing World. Tomales, CA: The Foundation for Critical Thinking.
• Paul, Richard and Linda Elder. 2006. The Miniature Guide to Critical Thinking Concepts and Tools, 4th ed. Tomales, CA: The Foundation for Critical Thinking.
• Plantinga, Alvin. 1974. The Nature of Necessity. Oxford Clarendon.
• Prior, Arthur. 1957. Time and Modality. Oxford, UK: Oxford University Press.
• Prior, Arthur. 1967. Past, Present and Future. Oxford, UK: Oxford University Press.
• Prior, Arthur. 1968. Papers on Time and Tense. Oxford, UK: Oxford University Press.
• Quine, W. V. O. and J. S. Ullian. 1978. The Web of Belief, 2nd ed. McGraw-Hill.
• Russell, Bertrand. 1940/1996. An Inquiry into Meaning and Truth, 2nd ed. London: Routledge.
• Thaler, Richard and Cass Sunstein. 2009. Nudge: Improving Decisions about Health, Wealth, and Happiness. New York: Penguin Books.
• van Eemeren, Frans H. and Rob Grootendorst. 1992. Argumentation, Communication, and Fallacies: A Pragma-Dialectical Perspective. London: Routledge.
• van Eemeren, Frans H. and Rob Grootendorst. 1995. “The Pragma-Dialectical Approach to Fallacies.” In Hans V. Hansen and Robert C. Pinto, eds. Fallacies: Classical and Contemporary Readings. Penn State University Press, 130-144.
• Vaughn, Lewis and Theodore Schick. 2010. How To Think About Weird Things: Critical Thinking for a New Age, 6th ed. McGraw-Hill.
• Walton, Douglas. 2008. Informal Logic: A Pragmatic Approach, 2nd ed. New York: Cambridge University Press.
• Watson, Jamie Carlin and Robert Arp. 2015. Critical Thinking: An Introduction to Reasoning Well, 2nd ed. London: Bloomsbury Academic.
• Weston, Anthony. 2008. A Rulebook for Arguments, 4th ed. Indianapolis: Hackett.
• Zadeh, Lofti. 1965. “Fuzzy Sets and Systems.” In J. Fox, ed., System Theory. Brooklyn, NY: Polytechnic Press, 29-39.

Author Information

Jamie Carlin Watson Email: [email protected] University of Arkansas for Medical Sciences U. S. A.

An encyclopedia of philosophy articles written by professional philosophers.

Thinking critically on critical thinking: why scientists’ skills need to spread

Lecturer in Psychology, University of Tasmania

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Rachel Grieve does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

University of Tasmania provides funding as a member of The Conversation AU.

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MATHS AND SCIENCE EDUCATION: We’ve asked our authors about the state of maths and science education in Australia and its future direction. Today, Rachel Grieve discusses why we need to spread science-specific skills into the wider curriculum.

When we think of science and maths, stereotypical visions of lab coats, test-tubes, and formulae often spring to mind.

But more important than these stereotypes are the methods that underpin the work scientists do – namely generating and systematically testing hypotheses. A key part of this is critical thinking.

It’s a skill that often feels in short supply these days, but you don’t necessarily need to study science or maths in order gain it. It’s time to take critical thinking out of the realm of maths and science and broaden it into students’ general education.

What is critical thinking?

Critical thinking is a reflective and analytical style of thinking, with its basis in logic, rationality, and synthesis. It means delving deeper and asking questions like: why is that so? Where is the evidence? How good is that evidence? Is this a good argument? Is it biased? Is it verifiable? What are the alternative explanations?

Critical thinking moves us beyond mere description and into the realms of scientific inference and reasoning. This is what enables discoveries to be made and innovations to be fostered.

For many scientists, critical thinking becomes (seemingly) intuitive, but like any skill set, critical thinking needs to be taught and cultivated. Unfortunately, educators are unable to deposit this information directly into their students’ heads. While the theory of critical thinking can be taught, critical thinking itself needs to be experienced first-hand.

So what does this mean for educators trying to incorporate critical thinking within their curricula? We can teach students the theoretical elements of critical thinking. Take for example working through [statistical problems](http://wdeneys.org/data/COGNIT_1695.pdf](http://wdeneys.org/data/COGNIT_1695.pdf) like this one:

In a 1,000-person study, four people said their favourite series was Star Trek and 996 said Days of Our Lives. Jeremy is a randomly chosen participant in this study, is 26, and is doing graduate studies in physics. He stays at home most of the time and likes to play videogames. What is most likely? a. Jeremy’s favourite series is Star Trek b. Jeremy’s favourite series is Days of Our Lives

Some critical thought applied to this problem allows us to know that Jeremy is most likely to prefer Days of Our Lives.

Can you teach it?

It’s well established that statistical training is associated with improved decision-making. But the idea of “teaching” critical thinking is itself an oxymoron: critical thinking can really only be learned through practice. Thus, it is not surprising that student engagement with the critical thinking process itself is what pays the dividends for students.

As such, educators try to connect students with the subject matter outside the lecture theatre or classroom. For example, problem based learning is now widely used in the health sciences, whereby students must figure out the key issues related to a case and direct their own learning to solve that problem. Problem based learning has clear parallels with real life practice for health professionals.

Critical thinking goes beyond what might be on the final exam and life-long learning becomes the key. This is a good thing, as practice helps to improve our ability to think critically over time .

Just for scientists?

For those engaging with science, learning the skills needed to be a critical consumer of information is invaluable. But should these skills remain in the domain of scientists? Clearly not: for those engaging with life, being a critical consumer of information is also invaluable, allowing informed judgement.

Being able to actively consider and evaluate information, identify biases, examine the logic of arguments, and tolerate ambiguity until the evidence is in would allow many people from all backgrounds to make better decisions. While these decisions can be trivial (does that miracle anti-wrinkle cream really do what it claims?), in many cases, reasoning and decision-making can have a substantial impact, with some decisions have life-altering effects. A timely case-in-point is immunisation.

Pushing critical thinking from the realms of science and maths into the broader curriculum may lead to far-reaching outcomes. With increasing access to information on the internet, giving individuals the skills to critically think about that information may have widespread benefit, both personally and socially.

The value of science education might not always be in the facts, but in the thinking.

This is the sixth part of our series Maths and Science Education .

• Maths and science education

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Understanding the Complex Relationship between Critical Thinking and Science Reasoning among Undergraduate Thesis Writers

• Jason E. Dowd
• Robert J. Thompson
• Leslie A. Schiff
• Julie A. Reynolds

*Address correspondence to: Jason E. Dowd ( E-mail Address: [email protected] ).

Department of Biology, Duke University, Durham, NC 27708

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Department of Psychology and Neuroscience, Duke University, Durham, NC 27708

Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN 55455

Developing critical-thinking and scientific reasoning skills are core learning objectives of science education, but little empirical evidence exists regarding the interrelationships between these constructs. Writing effectively fosters students’ development of these constructs, and it offers a unique window into studying how they relate. In this study of undergraduate thesis writing in biology at two universities, we examine how scientific reasoning exhibited in writing (assessed using the Biology Thesis Assessment Protocol) relates to general and specific critical-thinking skills (assessed using the California Critical Thinking Skills Test), and we consider implications for instruction. We find that scientific reasoning in writing is strongly related to inference , while other aspects of science reasoning that emerge in writing (epistemological considerations, writing conventions, etc.) are not significantly related to critical-thinking skills. Science reasoning in writing is not merely a proxy for critical thinking. In linking features of students’ writing to their critical-thinking skills, this study 1) provides a bridge to prior work suggesting that engagement in science writing enhances critical thinking and 2) serves as a foundational step for subsequently determining whether instruction focused explicitly on developing critical-thinking skills (particularly inference ) can actually improve students’ scientific reasoning in their writing.

INTRODUCTION

Critical-thinking and scientific reasoning skills are core learning objectives of science education for all students, regardless of whether or not they intend to pursue a career in science or engineering. Consistent with the view of learning as construction of understanding and meaning ( National Research Council, 2000 ), the pedagogical practice of writing has been found to be effective not only in fostering the development of students’ conceptual and procedural knowledge ( Gerdeman et al. , 2007 ) and communication skills ( Clase et al. , 2010 ), but also scientific reasoning ( Reynolds et al. , 2012 ) and critical-thinking skills ( Quitadamo and Kurtz, 2007 ).

Critical thinking and scientific reasoning are similar but different constructs that include various types of higher-order cognitive processes, metacognitive strategies, and dispositions involved in making meaning of information. Critical thinking is generally understood as the broader construct ( Holyoak and Morrison, 2005 ), comprising an array of cognitive processes and dispostions that are drawn upon differentially in everyday life and across domains of inquiry such as the natural sciences, social sciences, and humanities. Scientific reasoning, then, may be interpreted as the subset of critical-thinking skills (cognitive and metacognitive processes and dispositions) that 1) are involved in making meaning of information in scientific domains and 2) support the epistemological commitment to scientific methodology and paradigm(s).

Although there has been an enduring focus in higher education on promoting critical thinking and reasoning as general or “transferable” skills, research evidence provides increasing support for the view that reasoning and critical thinking are also situational or domain specific ( Beyer et al. , 2013 ). Some researchers, such as Lawson (2010) , present frameworks in which science reasoning is characterized explicitly in terms of critical-thinking skills. There are, however, limited coherent frameworks and empirical evidence regarding either the general or domain-specific interrelationships of scientific reasoning, as it is most broadly defined, and critical-thinking skills.

The Vision and Change in Undergraduate Biology Education Initiative provides a framework for thinking about these constructs and their interrelationship in the context of the core competencies and disciplinary practice they describe ( American Association for the Advancement of Science, 2011 ). These learning objectives aim for undergraduates to “understand the process of science, the interdisciplinary nature of the new biology and how science is closely integrated within society; be competent in communication and collaboration; have quantitative competency and a basic ability to interpret data; and have some experience with modeling, simulation and computational and systems level approaches as well as with using large databases” ( Woodin et al. , 2010 , pp. 71–72). This framework makes clear that science reasoning and critical-thinking skills play key roles in major learning outcomes; for example, “understanding the process of science” requires students to engage in (and be metacognitive about) scientific reasoning, and having the “ability to interpret data” requires critical-thinking skills. To help students better achieve these core competencies, we must better understand the interrelationships of their composite parts. Thus, the next step is to determine which specific critical-thinking skills are drawn upon when students engage in science reasoning in general and with regard to the particular scientific domain being studied. Such a determination could be applied to improve science education for both majors and nonmajors through pedagogical approaches that foster critical-thinking skills that are most relevant to science reasoning.

Writing affords one of the most effective means for making thinking visible ( Reynolds et al. , 2012 ) and learning how to “think like” and “write like” disciplinary experts ( Meizlish et al. , 2013 ). As a result, student writing affords the opportunities to both foster and examine the interrelationship of scientific reasoning and critical-thinking skills within and across disciplinary contexts. The purpose of this study was to better understand the relationship between students’ critical-thinking skills and scientific reasoning skills as reflected in the genre of undergraduate thesis writing in biology departments at two research universities, the University of Minnesota and Duke University.

In the following subsections, we discuss in greater detail the constructs of scientific reasoning and critical thinking, as well as the assessment of scientific reasoning in students’ thesis writing. In subsequent sections, we discuss our study design, findings, and the implications for enhancing educational practices.

Critical Thinking

The advances in cognitive science in the 21st century have increased our understanding of the mental processes involved in thinking and reasoning, as well as memory, learning, and problem solving. Critical thinking is understood to include both a cognitive dimension and a disposition dimension (e.g., reflective thinking) and is defined as “purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considera­tions upon which that judgment is based” ( Facione, 1990, p. 3 ). Although various other definitions of critical thinking have been proposed, researchers have generally coalesced on this consensus: expert view ( Blattner and Frazier, 2002 ; Condon and Kelly-Riley, 2004 ; Bissell and Lemons, 2006 ; Quitadamo and Kurtz, 2007 ) and the corresponding measures of critical-­thinking skills ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ).

Both the cognitive skills and dispositional components of critical thinking have been recognized as important to science education ( Quitadamo and Kurtz, 2007 ). Empirical research demonstrates that specific pedagogical practices in science courses are effective in fostering students’ critical-thinking skills. Quitadamo and Kurtz (2007) found that students who engaged in a laboratory writing component in the context of a general education biology course significantly improved their overall critical-thinking skills (and their analytical and inference skills, in particular), whereas students engaged in a traditional quiz-based laboratory did not improve their critical-thinking skills. In related work, Quitadamo et al. (2008) found that a community-based inquiry experience, involving inquiry, writing, research, and analysis, was associated with improved critical thinking in a biology course for nonmajors, compared with traditionally taught sections. In both studies, students who exhibited stronger presemester critical-thinking skills exhibited stronger gains, suggesting that “students who have not been explicitly taught how to think critically may not reach the same potential as peers who have been taught these skills” ( Quitadamo and Kurtz, 2007 , p. 151).

Recently, Stephenson and Sadler-McKnight (2016) found that first-year general chemistry students who engaged in a science writing heuristic laboratory, which is an inquiry-based, writing-to-learn approach to instruction ( Hand and Keys, 1999 ), had significantly greater gains in total critical-thinking scores than students who received traditional laboratory instruction. Each of the four components—inquiry, writing, collaboration, and reflection—have been linked to critical thinking ( Stephenson and Sadler-McKnight, 2016 ). Like the other studies, this work highlights the value of targeting critical-thinking skills and the effectiveness of an inquiry-based, writing-to-learn approach to enhance critical thinking. Across studies, authors advocate adopting critical thinking as the course framework ( Pukkila, 2004 ) and developing explicit examples of how critical thinking relates to the scientific method ( Miri et al. , 2007 ).

In these examples, the important connection between writing and critical thinking is highlighted by the fact that each intervention involves the incorporation of writing into science, technology, engineering, and mathematics education (either alone or in combination with other pedagogical practices). However, critical-thinking skills are not always the primary learning outcome; in some contexts, scientific reasoning is the primary outcome that is assessed.

Scientific Reasoning

Scientific reasoning is a complex process that is broadly defined as “the skills involved in inquiry, experimentation, evidence evaluation, and inference that are done in the service of conceptual change or scientific understanding” ( Zimmerman, 2007 , p. 172). Scientific reasoning is understood to include both conceptual knowledge and the cognitive processes involved with generation of hypotheses (i.e., inductive processes involved in the generation of hypotheses and the deductive processes used in the testing of hypotheses), experimentation strategies, and evidence evaluation strategies. These dimensions are interrelated, in that “experimentation and inference strategies are selected based on prior conceptual knowledge of the domain” ( Zimmerman, 2000 , p. 139). Furthermore, conceptual and procedural knowledge and cognitive process dimensions can be general and domain specific (or discipline specific).

With regard to conceptual knowledge, attention has been focused on the acquisition of core methodological concepts fundamental to scientists’ causal reasoning and metacognitive distancing (or decontextualized thinking), which is the ability to reason independently of prior knowledge or beliefs ( Greenhoot et al. , 2004 ). The latter involves what Kuhn and Dean (2004) refer to as the coordination of theory and evidence, which requires that one question existing theories (i.e., prior knowledge and beliefs), seek contradictory evidence, eliminate alternative explanations, and revise one’s prior beliefs in the face of contradictory evidence. Kuhn and colleagues (2008) further elaborate that scientific thinking requires “a mature understanding of the epistemological foundations of science, recognizing scientific knowledge as constructed by humans rather than simply discovered in the world,” and “the ability to engage in skilled argumentation in the scientific domain, with an appreciation of argumentation as entailing the coordination of theory and evidence” ( Kuhn et al. , 2008 , p. 435). “This approach to scientific reasoning not only highlights the skills of generating and evaluating evidence-based inferences, but also encompasses epistemological appreciation of the functions of evidence and theory” ( Ding et al. , 2016 , p. 616). Evaluating evidence-based inferences involves epistemic cognition, which Moshman (2015) defines as the subset of metacognition that is concerned with justification, truth, and associated forms of reasoning. Epistemic cognition is both general and domain specific (or discipline specific; Moshman, 2015 ).

There is empirical support for the contributions of both prior knowledge and an understanding of the epistemological foundations of science to scientific reasoning. In a study of undergraduate science students, advanced scientific reasoning was most often accompanied by accurate prior knowledge as well as sophisticated epistemological commitments; additionally, for students who had comparable levels of prior knowledge, skillful reasoning was associated with a strong epistemological commitment to the consistency of theory with evidence ( Zeineddin and Abd-El-Khalick, 2010 ). These findings highlight the importance of the need for instructional activities that intentionally help learners develop sophisticated epistemological commitments focused on the nature of knowledge and the role of evidence in supporting knowledge claims ( Zeineddin and Abd-El-Khalick, 2010 ).

Scientific Reasoning in Students’ Thesis Writing

Pedagogical approaches that incorporate writing have also focused on enhancing scientific reasoning. Many rubrics have been developed to assess aspects of scientific reasoning in written artifacts. For example, Timmerman and colleagues (2011) , in the course of describing their own rubric for assessing scientific reasoning, highlight several examples of scientific reasoning assessment criteria ( Haaga, 1993 ; Tariq et al. , 1998 ; Topping et al. , 2000 ; Kelly and Takao, 2002 ; Halonen et al. , 2003 ; Willison and O’Regan, 2007 ).

At both the University of Minnesota and Duke University, we have focused on the genre of the undergraduate honors thesis as the rhetorical context in which to study and improve students’ scientific reasoning and writing. We view the process of writing an undergraduate honors thesis as a form of professional development in the sciences (i.e., a way of engaging students in the practices of a community of discourse). We have found that structured courses designed to scaffold the thesis-­writing process and promote metacognition can improve writing and reasoning skills in biology, chemistry, and economics ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In the context of this prior work, we have defined scientific reasoning in writing as the emergent, underlying construct measured across distinct aspects of students’ written discussion of independent research in their undergraduate theses.

The Biology Thesis Assessment Protocol (BioTAP) was developed at Duke University as a tool for systematically guiding students and faculty through a “draft–feedback–revision” writing process, modeled after professional scientific peer-review processes ( Reynolds et al. , 2009 ). BioTAP includes activities and worksheets that allow students to engage in critical peer review and provides detailed descriptions, presented as rubrics, of the questions (i.e., dimensions, shown in Table 1 ) upon which such review should focus. Nine rubric dimensions focus on communication to the broader scientific community, and four rubric dimensions focus on the accuracy and appropriateness of the research. These rubric dimensions provide criteria by which the thesis is assessed, and therefore allow BioTAP to be used as an assessment tool as well as a teaching resource ( Reynolds et al. , 2009 ). Full details are available at www.science-writing.org/biotap.html .

In previous work, we have used BioTAP to quantitatively assess students’ undergraduate honors theses and explore the relationship between thesis-writing courses (or specific interventions within the courses) and the strength of students’ science reasoning in writing across different science disciplines: biology ( Reynolds and Thompson, 2011 ); chemistry ( Dowd et al. , 2015b ); and economics ( Dowd et al. , 2015a ). We have focused exclusively on the nine dimensions related to reasoning and writing (questions 1–9), as the other four dimensions (questions 10–13) require topic-specific expertise and are intended to be used by the student’s thesis supervisor.

Beyond considering individual dimensions, we have investigated whether meaningful constructs underlie students’ thesis scores. We conducted exploratory factor analysis of students’ theses in biology, economics, and chemistry and found one dominant underlying factor in each discipline; we termed the factor “scientific reasoning in writing” ( Dowd et al. , 2015a , b , 2016 ). That is, each of the nine dimensions could be understood as reflecting, in different ways and to different degrees, the construct of scientific reasoning in writing. The findings indicated evidence of both general and discipline-specific components to scientific reasoning in writing that relate to epistemic beliefs and paradigms, in keeping with broader ideas about science reasoning discussed earlier. Specifically, scientific reasoning in writing is more strongly associated with formulating a compelling argument for the significance of the research in the context of current literature in biology, making meaning regarding the implications of the findings in chemistry, and providing an organizational framework for interpreting the thesis in economics. We suggested that instruction, whether occurring in writing studios or in writing courses to facilitate thesis preparation, should attend to both components.

Research Question and Study Design

The genre of thesis writing combines the pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-­McKnight, 2016 ). However, there is no empirical evidence regarding the general or domain-specific interrelationships of scientific reasoning and critical-thinking skills, particularly in the rhetorical context of the undergraduate thesis. The BioTAP studies discussed earlier indicate that the rubric-based assessment produces evidence of scientific reasoning in the undergraduate thesis, but it was not designed to foster or measure critical thinking. The current study was undertaken to address the research question: How are students’ critical-thinking skills related to scientific reasoning as reflected in the genre of undergraduate thesis writing in biology? Determining these interrelationships could guide efforts to enhance students’ scientific reasoning and writing skills through focusing instruction on specific critical-thinking skills as well as disciplinary conventions.

To address this research question, we focused on undergraduate thesis writers in biology courses at two institutions, Duke University and the University of Minnesota, and examined the extent to which students’ scientific reasoning in writing, assessed in the undergraduate thesis using BioTAP, corresponds to students’ critical-thinking skills, assessed using the California Critical Thinking Skills Test (CCTST; August, 2016 ).

Study Sample

The study sample was composed of students enrolled in courses designed to scaffold the thesis-writing process in the Department of Biology at Duke University and the College of Biological Sciences at the University of Minnesota. Both courses complement students’ individual work with research advisors. The course is required for thesis writers at the University of Minnesota and optional for writers at Duke University. Not all students are required to complete a thesis, though it is required for students to graduate with honors; at the University of Minnesota, such students are enrolled in an honors program within the college. In total, 28 students were enrolled in the course at Duke University and 44 students were enrolled in the course at the University of Minnesota. Of those students, two students did not consent to participate in the study; additionally, five students did not validly complete the CCTST (i.e., attempted fewer than 60% of items or completed the test in less than 15 minutes). Thus, our overall rate of valid participation is 90%, with 27 students from Duke University and 38 students from the University of Minnesota. We found no statistically significant differences in thesis assessment between students with valid CCTST scores and invalid CCTST scores. Therefore, we focus on the 65 students who consented to participate and for whom we have complete and valid data in most of this study. Additionally, in asking students for their consent to participate, we allowed them to choose whether to provide or decline access to academic and demographic background data. Of the 65 students who consented to participate, 52 students granted access to such data. Therefore, for additional analyses involving academic and background data, we focus on the 52 students who consented. We note that the 13 students who participated but declined to share additional data performed slightly lower on the CCTST than the 52 others (perhaps suggesting that they differ by other measures, but we cannot determine this with certainty). Among the 52 students, 60% identified as female and 10% identified as being from underrepresented ethnicities.

In both courses, students completed the CCTST online, either in class or on their own, late in the Spring 2016 semester. This is the same assessment that was used in prior studies of critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). It is “an objective measure of the core reasoning skills needed for reflective decision making concerning what to believe or what to do” ( Insight Assessment, 2016a ). In the test, students are asked to read and consider information as they answer multiple-choice questions. The questions are intended to be appropriate for all users, so there is no expectation of prior disciplinary knowledge in biology (or any other subject). Although actual test items are protected, sample items are available on the Insight Assessment website ( Insight Assessment, 2016b ). We have included one sample item in the Supplemental Material.

The CCTST is based on a consensus definition of critical thinking, measures cognitive and metacognitive skills associated with critical thinking, and has been evaluated for validity and reliability at the college level ( August, 2016 ; Stephenson and Sadler-McKnight, 2016 ). In addition to providing overall critical-thinking score, the CCTST assesses seven dimensions of critical thinking: analysis, interpretation, inference, evaluation, explanation, induction, and deduction. Scores on each dimension are calculated based on students’ performance on items related to that dimension. Analysis focuses on identifying assumptions, reasons, and claims and examining how they interact to form arguments. Interpretation, related to analysis, focuses on determining the precise meaning and significance of information. Inference focuses on drawing conclusions from reasons and evidence. Evaluation focuses on assessing the credibility of sources of information and claims they make. Explanation, related to evaluation, focuses on describing the evidence, assumptions, or rationale for beliefs and conclusions. Induction focuses on drawing inferences about what is probably true based on evidence. Deduction focuses on drawing conclusions about what must be true when the context completely determines the outcome. These are not independent dimensions; the fact that they are related supports their collective interpretation as critical thinking. Together, the CCTST dimensions provide a basis for evaluating students’ overall strength in using reasoning to form reflective judgments about what to believe or what to do ( August, 2016 ). Each of the seven dimensions and the overall CCTST score are measured on a scale of 0–100, where higher scores indicate superior performance. Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and below) skills.

Scientific Reasoning in Writing

At the end of the semester, students’ final, submitted undergraduate theses were assessed using BioTAP, which consists of nine rubric dimensions that focus on communication to the broader scientific community and four additional dimensions that focus on the exhibition of topic-specific expertise ( Reynolds et al. , 2009 ). These dimensions, framed as questions, are displayed in Table 1 .

Student theses were assessed on questions 1–9 of BioTAP using the same procedures described in previous studies ( Reynolds and Thompson, 2011 ; Dowd et al. , 2015a , b ). In this study, six raters were trained in the valid, reliable use of BioTAP rubrics. Each dimension was rated on a five-point scale: 1 indicates the dimension is missing, incomplete, or below acceptable standards; 3 indicates that the dimension is adequate but not exhibiting mastery; and 5 indicates that the dimension is excellent and exhibits mastery (intermediate ratings of 2 and 4 are appropriate when different parts of the thesis make a single category challenging). After training, two raters independently assessed each thesis and then discussed their independent ratings with one another to form a consensus rating. The consensus score is not an average score, but rather an agreed-upon, discussion-based score. On a five-point scale, raters independently assessed dimensions to be within 1 point of each other 82.4% of the time before discussion and formed consensus ratings 100% of the time after discussion.

In this study, we consider both categorical (mastery/nonmastery, where a score of 5 corresponds to mastery) and numerical treatments of individual BioTAP scores to better relate the manifestation of critical thinking in BioTAP assessment to all of the prior studies. For comprehensive/cumulative measures of BioTAP, we focus on the partial sum of questions 1–5, as these questions relate to higher-order scientific reasoning (whereas questions 6–9 relate to mid- and lower-order writing mechanics [ Reynolds et al. , 2009 ]), and the factor scores (i.e., numerical representations of the extent to which each student exhibits the underlying factor), which are calculated from the factor loadings published by Dowd et al. (2016) . We do not focus on questions 6–9 individually in statistical analyses, because we do not expect critical-thinking skills to relate to mid- and lower-order writing skills.

The final, submitted thesis reflects the student’s writing, the student’s scientific reasoning, the quality of feedback provided to the student by peers and mentors, and the student’s ability to incorporate that feedback into his or her work. Therefore, our assessment is not the same as an assessment of unpolished, unrevised samples of students’ written work. While one might imagine that such an unpolished sample may be more strongly correlated with critical-thinking skills measured by the CCTST, we argue that the complete, submitted thesis, assessed using BioTAP, is ultimately a more appropriate reflection of how students exhibit science reasoning in the scientific community.

Statistical Analyses

We took several steps to analyze the collected data. First, to provide context for subsequent interpretations, we generated descriptive statistics for the CCTST scores of the participants based on the norms for undergraduate CCTST test takers. To determine the strength of relationships among CCTST dimensions (including overall score) and the BioTAP dimensions, partial-sum score (questions 1–5), and factor score, we calculated Pearson’s correlations for each pair of measures. To examine whether falling on one side of the nonmastery/mastery threshold (as opposed to a linear scale of performance) was related to critical thinking, we grouped BioTAP dimensions into categories (mastery/nonmastery) and conducted Student’s t tests to compare the means scores of the two groups on each of the seven dimensions and overall score of the CCTST. Finally, for the strongest relationship that emerged, we included additional academic and background variables as covariates in multiple linear-regression analysis to explore questions about how much observed relationships between critical-thinking skills and science reasoning in writing might be explained by variation in these other factors.

Although BioTAP scores represent discreet, ordinal bins, the five-point scale is intended to capture an underlying continuous construct (from inadequate to exhibiting mastery). It has been argued that five categories is an appropriate cutoff for treating ordinal variables as pseudo-continuous ( Rhemtulla et al. , 2012 )—and therefore using continuous-variable statistical methods (e.g., Pearson’s correlations)—as long as the underlying assumption that ordinal scores are linearly distributed is valid. Although we have no way to statistically test this assumption, we interpret adequate scores to be approximately halfway between inadequate and mastery scores, resulting in a linear scale. In part because this assumption is subject to disagreement, we also consider and interpret a categorical (mastery/nonmastery) treatment of BioTAP variables.

We corrected for multiple comparisons using the Holm-Bonferroni method ( Holm, 1979 ). At the most general level, where we consider the single, comprehensive measures for BioTAP (partial-sum and factor score) and the CCTST (overall score), there is no need to correct for multiple comparisons, because the multiple, individual dimensions are collapsed into single dimensions. When we considered individual CCTST dimensions in relation to comprehensive measures for BioTAP, we accounted for seven comparisons; similarly, when we considered individual dimensions of BioTAP in relation to overall CCTST score, we accounted for five comparisons. When all seven CCTST and five BioTAP dimensions were examined individually and without prior knowledge, we accounted for 35 comparisons; such a rigorous threshold is likely to reject weak and moderate relationships, but it is appropriate if there are no specific pre-existing hypotheses. All p values are presented in tables for complete transparency, and we carefully consider the implications of our interpretation of these data in the Discussion section.

CCTST scores for students in this sample ranged from the 39th to 99th percentile of the general population of undergraduate CCTST test takers (mean percentile = 84.3, median = 85th percentile; Table 2 ); these percentiles reflect overall scores that range from moderate to superior. Scores on individual dimensions and overall scores were sufficiently normal and far enough from the ceiling of the scale to justify subsequent statistical analyses.

a Scores correspond to superior (86–100), strong (79–85), moderate (70–78), weak (63–69), or not manifested (62 and lower) skills.

The Pearson’s correlations between students’ cumulative scores on BioTAP (the factor score based on loadings published by Dowd et al. , 2016 , and the partial sum of scores on questions 1–5) and students’ overall scores on the CCTST are presented in Table 3 . We found that the partial-sum measure of BioTAP was significantly related to the overall measure of critical thinking ( r = 0.27, p = 0.03), while the BioTAP factor score was marginally related to overall CCTST ( r = 0.24, p = 0.05). When we looked at relationships between comprehensive BioTAP measures and scores for individual dimensions of the CCTST ( Table 3 ), we found significant positive correlations between the both BioTAP partial-sum and factor scores and CCTST inference ( r = 0.45, p < 0.001, and r = 0.41, p < 0.001, respectively). Although some other relationships have p values below 0.05 (e.g., the correlations between BioTAP partial-sum scores and CCTST induction and interpretation scores), they are not significant when we correct for multiple comparisons.

a In each cell, the top number is the correlation, and the bottom, italicized number is the associated p value. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

b This is the partial sum of BioTAP scores on questions 1–5.

c This is the factor score calculated from factor loadings published by Dowd et al. (2016) .

When we expanded comparisons to include all 35 potential correlations among individual BioTAP and CCTST dimensions—and, accordingly, corrected for 35 comparisons—we did not find any additional statistically significant relationships. The Pearson’s correlations between students’ scores on each dimension of BioTAP and students’ scores on each dimension of the CCTST range from −0.11 to 0.35 ( Table 3 ); although the relationship between discussion of implications (BioTAP question 5) and inference appears to be relatively large ( r = 0.35), it is not significant ( p = 0.005; the Holm-Bonferroni cutoff is 0.00143). We found no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions (unpublished data), regardless of whether we correct for multiple comparisons.

The results of Student’s t tests comparing scores on each dimension of the CCTST of students who exhibit mastery with those of students who do not exhibit mastery on each dimension of BioTAP are presented in Table 4 . Focusing first on the overall CCTST scores, we found that the difference between those who exhibit mastery and those who do not in discussing implications of results (BioTAP question 5) is statistically significant ( t = 2.73, p = 0.008, d = 0.71). When we expanded t tests to include all 35 comparisons—and, like above, corrected for 35 comparisons—we found a significant difference in inference scores between students who exhibit mastery on question 5 and students who do not ( t = 3.41, p = 0.0012, d = 0.88), as well as a marginally significant difference in these students’ induction scores ( t = 3.26, p = 0.0018, d = 0.84; the Holm-Bonferroni cutoff is p = 0.00147). Cohen’s d effect sizes, which reveal the strength of the differences for statistically significant relationships, range from 0.71 to 0.88.

a In each cell, the top number is the t statistic for each comparison, and the middle, italicized number is the associated p value. The bottom number is the effect size. Correlations that are statistically significant after correcting for multiple comparisons are shown in bold.

Finally, we more closely examined the strongest relationship that we observed, which was between the CCTST dimension of inference and the BioTAP partial-sum composite score (shown in Table 3 ), using multiple regression analysis ( Table 5 ). Focusing on the 52 students for whom we have background information, we looked at the simple relationship between BioTAP and inference (model 1), a robust background model including multiple covariates that one might expect to explain some part of the variation in BioTAP (model 2), and a combined model including all variables (model 3). As model 3 shows, the covariates explain very little variation in BioTAP scores, and the relationship between inference and BioTAP persists even in the presence of all of the covariates.

** p < 0.01.

*** p < 0.001.

The aim of this study was to examine the extent to which the various components of scientific reasoning—manifested in writing in the genre of undergraduate thesis and assessed using BioTAP—draw on general and specific critical-thinking skills (assessed using CCTST) and to consider the implications for educational practices. Although science reasoning involves critical-thinking skills, it also relates to conceptual knowledge and the epistemological foundations of science disciplines ( Kuhn et al. , 2008 ). Moreover, science reasoning in writing , captured in students’ undergraduate theses, reflects habits, conventions, and the incorporation of feedback that may alter evidence of individuals’ critical-thinking skills. Our findings, however, provide empirical evidence that cumulative measures of science reasoning in writing are nonetheless related to students’ overall critical-thinking skills ( Table 3 ). The particularly significant roles of inference skills ( Table 3 ) and the discussion of implications of results (BioTAP question 5; Table 4 ) provide a basis for more specific ideas about how these constructs relate to one another and what educational interventions may have the most success in fostering these skills.

Our results build on previous findings. The genre of thesis writing combines pedagogies of writing and inquiry found to foster scientific reasoning ( Reynolds et al. , 2012 ) and critical thinking ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ; Stephenson and Sadler-McKnight, 2016 ). Quitadamo and Kurtz (2007) reported that students who engaged in a laboratory writing component in a general education biology course significantly improved their inference and analysis skills, and Quitadamo and colleagues (2008) found that participation in a community-based inquiry biology course (that included a writing component) was associated with significant gains in students’ inference and evaluation skills. The shared focus on inference is noteworthy, because these prior studies actually differ from the current study; the former considered critical-­thinking skills as the primary learning outcome of writing-­focused interventions, whereas the latter focused on emergent links between two learning outcomes (science reasoning in writing and critical thinking). In other words, inference skills are impacted by writing as well as manifested in writing.

Inference focuses on drawing conclusions from argument and evidence. According to the consensus definition of critical thinking, the specific skill of inference includes several processes: querying evidence, conjecturing alternatives, and drawing conclusions. All of these activities are central to the independent research at the core of writing an undergraduate thesis. Indeed, a critical part of what we call “science reasoning in writing” might be characterized as a measure of students’ ability to infer and make meaning of information and findings. Because the cumulative BioTAP measures distill underlying similarities and, to an extent, suppress unique aspects of individual dimensions, we argue that it is appropriate to relate inference to scientific reasoning in writing . Even when we control for other potentially relevant background characteristics, the relationship is strong ( Table 5 ).

In taking the complementary view and focusing on BioTAP, when we compared students who exhibit mastery with those who do not, we found that the specific dimension of “discussing the implications of results” (question 5) differentiates students’ performance on several critical-thinking skills. To achieve mastery on this dimension, students must make connections between their results and other published studies and discuss the future directions of the research; in short, they must demonstrate an understanding of the bigger picture. The specific relationship between question 5 and inference is the strongest observed among all individual comparisons. Altogether, perhaps more than any other BioTAP dimension, this aspect of students’ writing provides a clear view of the role of students’ critical-thinking skills (particularly inference and, marginally, induction) in science reasoning.

While inference and discussion of implications emerge as particularly strongly related dimensions in this work, we note that the strongest contribution to “science reasoning in writing in biology,” as determined through exploratory factor analysis, is “argument for the significance of research” (BioTAP question 2, not question 5; Dowd et al. , 2016 ). Question 2 is not clearly related to critical-thinking skills. These findings are not contradictory, but rather suggest that the epistemological and disciplinary-specific aspects of science reasoning that emerge in writing through BioTAP are not completely aligned with aspects related to critical thinking. In other words, science reasoning in writing is not simply a proxy for those critical-thinking skills that play a role in science reasoning.

In a similar vein, the content-related, epistemological aspects of science reasoning, as well as the conventions associated with writing the undergraduate thesis (including feedback from peers and revision), may explain the lack of significant relationships between some science reasoning dimensions and some critical-thinking skills that might otherwise seem counterintuitive (e.g., BioTAP question 2, which relates to making an argument, and the critical-thinking skill of argument). It is possible that an individual’s critical-thinking skills may explain some variation in a particular BioTAP dimension, but other aspects of science reasoning and practice exert much stronger influence. Although these relationships do not emerge in our analyses, the lack of significant correlation does not mean that there is definitively no correlation. Correcting for multiple comparisons suppresses type 1 error at the expense of exacerbating type 2 error, which, combined with the limited sample size, constrains statistical power and makes weak relationships more difficult to detect. Ultimately, though, the relationships that do emerge highlight places where individuals’ distinct critical-thinking skills emerge most coherently in thesis assessment, which is why we are particularly interested in unpacking those relationships.

We recognize that, because only honors students submit theses at these institutions, this study sample is composed of a selective subset of the larger population of biology majors. Although this is an inherent limitation of focusing on thesis writing, links between our findings and results of other studies (with different populations) suggest that observed relationships may occur more broadly. The goal of improved science reasoning and critical thinking is shared among all biology majors, particularly those engaged in capstone research experiences. So while the implications of this work most directly apply to honors thesis writers, we provisionally suggest that all students could benefit from further study of them.

There are several important implications of this study for science education practices. Students’ inference skills relate to the understanding and effective application of scientific content. The fact that we find no statistically significant relationships between BioTAP questions 6–9 and CCTST dimensions suggests that such mid- to lower-order elements of BioTAP ( Reynolds et al. , 2009 ), which tend to be more structural in nature, do not focus on aspects of the finished thesis that draw strongly on critical thinking. In keeping with prior analyses ( Reynolds and Thompson, 2011 ; Dowd et al. , 2016 ), these findings further reinforce the notion that disciplinary instructors, who are most capable of teaching and assessing scientific reasoning and perhaps least interested in the more mechanical aspects of writing, may nonetheless be best suited to effectively model and assess students’ writing.

The goal of the thesis writing course at both Duke University and the University of Minnesota is not merely to improve thesis scores but to move students’ writing into the category of mastery across BioTAP dimensions. Recognizing that students with differing critical-thinking skills (particularly inference) are more or less likely to achieve mastery in the undergraduate thesis (particularly in discussing implications [question 5]) is important for developing and testing targeted pedagogical interventions to improve learning outcomes for all students.

The competencies characterized by the Vision and Change in Undergraduate Biology Education Initiative provide a general framework for recognizing that science reasoning and critical-thinking skills play key roles in major learning outcomes of science education. Our findings highlight places where science reasoning–related competencies (like “understanding the process of science”) connect to critical-thinking skills and places where critical thinking–related competencies might be manifested in scientific products (such as the ability to discuss implications in scientific writing). We encourage broader efforts to build empirical connections between competencies and pedagogical practices to further improve science education.

One specific implication of this work for science education is to focus on providing opportunities for students to develop their critical-thinking skills (particularly inference). Of course, as this correlational study is not designed to test causality, we do not claim that enhancing students’ inference skills will improve science reasoning in writing. However, as prior work shows that science writing activities influence students’ inference skills ( Quitadamo and Kurtz, 2007 ; Quitadamo et al. , 2008 ), there is reason to test such a hypothesis. Nevertheless, the focus must extend beyond inference as an isolated skill; rather, it is important to relate inference to the foundations of the scientific method ( Miri et al. , 2007 ) in terms of the epistemological appreciation of the functions and coordination of evidence ( Kuhn and Dean, 2004 ; Zeineddin and Abd-El-Khalick, 2010 ; Ding et al. , 2016 ) and disciplinary paradigms of truth and justification ( Moshman, 2015 ).

Although this study is limited to the domain of biology at two institutions with a relatively small number of students, the findings represent a foundational step in the direction of achieving success with more integrated learning outcomes. Hopefully, it will spur greater interest in empirically grounding discussions of the constructs of scientific reasoning and critical-thinking skills.

This study contributes to the efforts to improve science education, for both majors and nonmajors, through an empirically driven analysis of the relationships between scientific reasoning reflected in the genre of thesis writing and critical-thinking skills. This work is rooted in the usefulness of BioTAP as a method 1) to facilitate communication and learning and 2) to assess disciplinary-specific and general dimensions of science reasoning. The findings support the important role of the critical-thinking skill of inference in scientific reasoning in writing, while also highlighting ways in which other aspects of science reasoning (epistemological considerations, writing conventions, etc.) are not significantly related to critical thinking. Future research into the impact of interventions focused on specific critical-thinking skills (i.e., inference) for improved science reasoning in writing will build on this work and its implications for science education.

ACKNOWLEDGMENTS

We acknowledge the contributions of Kelaine Haas and Alexander Motten to the implementation and collection of data. We also thank Mine Çetinkaya-­Rundel for her insights regarding our statistical analyses. This research was funded by National Science Foundation award DUE-1525602.

• American Association for the Advancement of Science . ( 2011 ). Vision and change in undergraduate biology education: A call to action . Washington, DC Retrieved September 26, 2017, from https://visionandchange.org/files/2013/11/aaas-VISchange-web1113.pdf . Google Scholar
• August, D. ( 2016 ). California Critical Thinking Skills Test user manual and resource guide . San Jose: Insight Assessment/California Academic Press. Google Scholar
• Beyer, C. H., Taylor, E., & Gillmore, G. M. ( 2013 ). Inside the undergraduate teaching experience: The University of Washington’s growth in faculty teaching study . Albany, NY: SUNY Press. Google Scholar
• Bissell, A. N., & Lemons, P. P. ( 2006 ). A new method for assessing critical thinking in the classroom . BioScience , 56 (1), 66–72. https://doi.org/10.1641/0006-3568(2006)056[0066:ANMFAC]2.0.CO;2 . Google Scholar
• Blattner, N. H., & Frazier, C. L. ( 2002 ). Developing a performance-based assessment of students’ critical thinking skills . Assessing Writing , 8 (1), 47–64. Google Scholar
• Clase, K. L., Gundlach, E., & Pelaez, N. J. ( 2010 ). Calibrated peer review for computer-assisted learning of biological research competencies . Biochemistry and Molecular Biology Education , 38 (5), 290–295. Medline ,  Google Scholar
• Condon, W., & Kelly-Riley, D. ( 2004 ). Assessing and teaching what we value: The relationship between college-level writing and critical thinking abilities . Assessing Writing , 9 (1), 56–75. https://doi.org/10.1016/j.asw.2004.01.003 . Google Scholar
• Ding, L., Wei, X., & Liu, X. ( 2016 ). Variations in university students’ scientific reasoning skills across majors, years, and types of institutions . Research in Science Education , 46 (5), 613–632. https://doi.org/10.1007/s11165-015-9473-y . Google Scholar
• Dowd, J. E., Connolly, M. P., Thompson, R. J.Jr., & Reynolds, J. A. ( 2015a ). Improved reasoning in undergraduate writing through structured workshops . Journal of Economic Education , 46 (1), 14–27. https://doi.org/10.1080/00220485.2014.978924 . Google Scholar
• Dowd, J. E., Roy, C. P., Thompson, R. J.Jr., & Reynolds, J. A. ( 2015b ). “On course” for supporting expanded participation and improving scientific reasoning in undergraduate thesis writing . Journal of Chemical Education , 92 (1), 39–45. https://doi.org/10.1021/ed500298r . Google Scholar
• Dowd, J. E., Thompson, R. J.Jr., & Reynolds, J. A. ( 2016 ). Quantitative genre analysis of undergraduate theses: Uncovering different ways of writing and thinking in science disciplines . WAC Journal , 27 , 36–51. Google Scholar
• Facione, P. A. ( 1990 ). Critical thinking: a statement of expert consensus for purposes of educational assessment and instruction. Research findings and recommendations . Newark, DE: American Philosophical Association. Retrieved September 26, 2017, from https://philpapers.org/archive/FACCTA.pdf . Google Scholar
• Gerdeman, R. D., Russell, A. A., Worden, K. J., Gerdeman, R. D., Russell, A. A., & Worden, K. J. ( 2007 ). Web-based student writing and reviewing in a large biology lecture course . Journal of College Science Teaching , 36 (5), 46–52. Google Scholar
• Greenhoot, A. F., Semb, G., Colombo, J., & Schreiber, T. ( 2004 ). Prior beliefs and methodological concepts in scientific reasoning . Applied Cognitive Psychology , 18 (2), 203–221. https://doi.org/10.1002/acp.959 . Google Scholar
• Haaga, D. A. F. ( 1993 ). Peer review of term papers in graduate psychology courses . Teaching of Psychology , 20 (1), 28–32. https://doi.org/10.1207/s15328023top2001_5 . Google Scholar
• Halonen, J. S., Bosack, T., Clay, S., McCarthy, M., Dunn, D. S., Hill, G. W., … Whitlock, K. ( 2003 ). A rubric for learning, teaching, and assessing scientific inquiry in psychology . Teaching of Psychology , 30 (3), 196–208. https://doi.org/10.1207/S15328023TOP3003_01 . Google Scholar
• Hand, B., & Keys, C. W. ( 1999 ). Inquiry investigation . Science Teacher , 66 (4), 27–29. Google Scholar
• Holm, S. ( 1979 ). A simple sequentially rejective multiple test procedure . Scandinavian Journal of Statistics , 6 (2), 65–70. Google Scholar
• Holyoak, K. J., & Morrison, R. G. ( 2005 ). The Cambridge handbook of thinking and reasoning . New York: Cambridge University Press. Google Scholar
• Insight Assessment . ( 2016a ). California Critical Thinking Skills Test (CCTST) Retrieved September 26, 2017, from www.insightassessment.com/Products/Products-Summary/Critical-Thinking-Skills-Tests/California-Critical-Thinking-Skills-Test-CCTST . Google Scholar
• Insight Assessment . ( 2016b ). Sample thinking skills questions. Retrieved September 26, 2017, from www.insightassessment.com/Resources/Teaching-Training-and-Learning-Tools/node_1487 . Google Scholar
• Kelly, G. J., & Takao, A. ( 2002 ). Epistemic levels in argument: An analysis of university oceanography students’ use of evidence in writing . Science Education , 86 (3), 314–342. https://doi.org/10.1002/sce.10024 . Google Scholar
• Kuhn, D., & Dean, D.Jr. ( 2004 ). Connecting scientific reasoning and causal inference . Journal of Cognition and Development , 5 (2), 261–288. https://doi.org/10.1207/s15327647jcd0502_5 . Google Scholar
• Kuhn, D., Iordanou, K., Pease, M., & Wirkala, C. ( 2008 ). Beyond control of variables: What needs to develop to achieve skilled scientific thinking? . Cognitive Development , 23 (4), 435–451. https://doi.org/10.1016/j.cogdev.2008.09.006 . Google Scholar
• Lawson, A. E. ( 2010 ). Basic inferences of scientific reasoning, argumentation, and discovery . Science Education , 94 (2), 336–364. https://doi.org/­10.1002/sce.20357 . Google Scholar
• Meizlish, D., LaVaque-Manty, D., Silver, N., & Kaplan, M. ( 2013 ). Think like/write like: Metacognitive strategies to foster students’ development as disciplinary thinkers and writers . In Thompson, R. J. (Ed.), Changing the conversation about higher education (pp. 53–73). Lanham, MD: Rowman & Littlefield. Google Scholar
• Miri, B., David, B.-C., & Uri, Z. ( 2007 ). Purposely teaching for the promotion of higher-order thinking skills: A case of critical thinking . Research in Science Education , 37 (4), 353–369. https://doi.org/10.1007/s11165-006-9029-2 . Google Scholar
• Moshman, D. ( 2015 ). Epistemic cognition and development: The psychology of justification and truth . New York: Psychology Press. Google Scholar
• National Research Council . ( 2000 ). How people learn: Brain, mind, experience, and school . Expanded ed.. Washington, DC: National Academies Press. Google Scholar
• Pukkila, P. J. ( 2004 ). Introducing student inquiry in large introductory genetics classes . Genetics , 166 (1), 11–18. https://doi.org/10.1534/genetics.166.1.11 . Medline ,  Google Scholar
• Quitadamo, I. J., Faiola, C. L., Johnson, J. E., & Kurtz, M. J. ( 2008 ). Community-based inquiry improves critical thinking in general education biology . CBE—Life Sciences Education , 7 (3), 327–337. https://doi.org/10.1187/cbe.07-11-0097 . Link ,  Google Scholar
• Quitadamo, I. J., & Kurtz, M. J. ( 2007 ). Learning to improve: Using writing to increase critical thinking performance in general education biology . CBE—Life Sciences Education , 6 (2), 140–154. https://doi.org/10.1187/cbe.06-11-0203 . Link ,  Google Scholar
• Reynolds, J. A., Smith, R., Moskovitz, C., & Sayle, A. ( 2009 ). BioTAP: A systematic approach to teaching scientific writing and evaluating undergraduate theses . BioScience , 59 (10), 896–903. https://doi.org/10.1525/bio.2009.59.10.11 . Google Scholar
• Reynolds, J. A., Thaiss, C., Katkin, W., & Thompson, R. J. ( 2012 ). Writing-to-learn in undergraduate science education: A community-based, conceptually driven approach . CBE—Life Sciences Education , 11 (1), 17–25. https://doi.org/10.1187/cbe.11-08-0064 . Link ,  Google Scholar
• Reynolds, J. A., & Thompson, R. J. ( 2011 ). Want to improve undergraduate thesis writing? Engage students and their faculty readers in scientific peer review . CBE—Life Sciences Education , 10 (2), 209–215. https://doi.org/­10.1187/cbe.10-10-0127 . Link ,  Google Scholar
• Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. ( 2012 ). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions . Psychological Methods , 17 (3), 354–373. https://doi.org/­10.1037/a0029315 . Medline ,  Google Scholar
• Stephenson, N. S., & Sadler-McKnight, N. P. ( 2016 ). Developing critical thinking skills using the science writing heuristic in the chemistry laboratory . Chemistry Education Research and Practice , 17 (1), 72–79. https://doi.org/­10.1039/C5RP00102A . Google Scholar
• Tariq, V. N., Stefani, L. A. J., Butcher, A. C., & Heylings, D. J. A. ( 1998 ). Developing a new approach to the assessment of project work . Assessment and Evaluation in Higher Education , 23 (3), 221–240. https://doi.org/­10.1080/0260293980230301 . Google Scholar
• Timmerman, B. E. C., Strickland, D. C., Johnson, R. L., & Payne, J. R. ( 2011 ). Development of a “universal” rubric for assessing undergraduates’ scientific reasoning skills using scientific writing . Assessment and Evaluation in Higher Education , 36 (5), 509–547. https://doi.org/10.1080/­02602930903540991 . Google Scholar
• Topping, K. J., Smith, E. F., Swanson, I., & Elliot, A. ( 2000 ). Formative peer assessment of academic writing between postgraduate students . Assessment and Evaluation in Higher Education , 25 (2), 149–169. https://doi.org/10.1080/713611428 . Google Scholar
• Willison, J., & O’Regan, K. ( 2007 ). Commonly known, commonly not known, totally unknown: A framework for students becoming researchers . Higher Education Research and Development , 26 (4), 393–409. https://doi.org/10.1080/07294360701658609 . Google Scholar
• Woodin, T., Carter, V. C., & Fletcher, L. ( 2010 ). Vision and Change in Biology Undergraduate Education: A Call for Action—Initial responses . CBE—Life Sciences Education , 9 (2), 71–73. https://doi.org/10.1187/cbe.10-03-0044 . Link ,  Google Scholar
• Zeineddin, A., & Abd-El-Khalick, F. ( 2010 ). Scientific reasoning and epistemological commitments: Coordination of theory and evidence among college science students . Journal of Research in Science Teaching , 47 (9), 1064–1093. https://doi.org/10.1002/tea.20368 . Google Scholar
• Zimmerman, C. ( 2000 ). The development of scientific reasoning skills . Developmental Review , 20 (1), 99–149. https://doi.org/10.1006/drev.1999.0497 . Google Scholar
• Zimmerman, C. ( 2007 ). The development of scientific thinking skills in elementary and middle school . Developmental Review , 27 (2), 172–223. https://doi.org/10.1016/j.dr.2006.12.001 . Google Scholar
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• Jason E. Dowd ,
• Robert J. Thompson ,
• Leslie Schiff ,
• Kelaine Haas ,
• Christine Hohmann ,
• Chris Roy ,
• Warren Meck ,
• John Bruno , and
• Rebecca Price, Monitoring Editor
• Kari L. Nelson ,
• Claudia M. Rauter , and
• Christine E. Cutucache
• Elisabeth Schussler, Monitoring Editor

Submitted: 17 March 2017 Revised: 19 October 2017 Accepted: 20 October 2017

© 2018 J. E. Dowd et al. CBE—Life Sciences Education © 2018 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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Our Conception of Critical Thinking...

"Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action. In its exemplary form, it is based on universal intellectual values that transcend subject matter divisions: clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness..."

"Critical thinking is self-guided, self-disciplined thinking which attempts to reason at the highest level of quality in a fairminded way. People who think critically attempt, with consistent and conscious effort, to live rationally, reasonably, and empathically. They are keenly aware of the inherently flawed nature of human thinking when left unchecked. They strive to diminish the power of their egocentric and sociocentric tendencies. They use the intellectual tools that critical thinking offers – concepts and principles that enable them to analyze, assess, and improve thinking. They work diligently to develop the intellectual virtues of intellectual integrity, intellectual humility, intellectual civility, intellectual empathy, intellectual sense of justice and confidence in reason. They realize that no matter how skilled they are as thinkers, they can always improve their reasoning abilities and they will at times fall prey to mistakes in reasoning, human irrationality, prejudices, biases, distortions, uncritically accepted social rules and taboos, self-interest, and vested interest.

They strive to improve the world in whatever ways they can and contribute to a more rational, civilized society. At the same time, they recognize the complexities often inherent in doing so. They strive never to think simplistically about complicated issues and always to consider the rights and needs of relevant others. They recognize the complexities in developing as thinkers, and commit themselves to life-long practice toward self-improvement. They embody the Socratic principle: The unexamined life is not worth living , because they realize that many unexamined lives together result in an uncritical, unjust, dangerous world."

Why Critical Thinking?

The Problem:

Everyone thinks; it is our nature to do so. But much of our thinking, left to itself, is biased, distorted, partial, uninformed, or down-right prejudiced. Yet the quality of our lives and that of what we produce, make, or build depends precisely on the quality of our thought. Shoddy thinking is costly, both in money and in quality of life. Excellence in thought, however, must be systematically cultivated.

A Brief Definition:

Critical thinking is the art of analyzing and evaluating thinking with a view to improving it. The Result: 

  A well-cultivated critical thinker:

• raises vital questions and problems, formulating them clearly and precisely;
• gathers and assesses relevant information, using abstract ideas to interpret it effectively;
• comes to well-reasoned conclusions and solutions, testing them against relevant criteria and standards;
• thinks openmindedly within alternative systems of thought, recognizing and assessing, as need be, their assumptions, implications, and practical consequences; and
• communicates effectively with others in figuring out solutions to complex problems.

Critical thinking is, in short, self-directed, self-disciplined, self-monitored, and self-corrective thinking. It requires rigorous standards of excellence and mindful command of their use. It entails effective communication and problem-solving abilities, and a commitment to overcoming our native egocentrism and sociocentrism. Read more about our concept of critical thinking .

The Essential Dimensions of Critical Thinking

Our conception of critical thinking is based on the substantive approach developed by Dr. Richard Paul and his colleagues at the Center and Foundation for Critical Thinking over multiple decades. It is relevant to every subject, discipline, and profession, and to reasoning through the problems of everyday life. It entails five essential dimensions of critical thinking:

At the left is an overview of the first three dimensions. In sum, the elements or structures of thought enable us to "take our thinking apart" and analyze it. The intellectual standards are used to assess and evaluate the elements. The intellectual traits are dispositions of mind embodied by the fairminded critical thinker. To cultivate the mind, we need command of these essential dimensions, and we need to consistently apply them as we think through the many problems and issues in our lives.

The Elements of Reasoning and Intellectual Standards

To learn more about the elements of thought and how to apply the intellectual standards, check out our interactive model. Simply click on the link below, scroll to the bottom of the page, and explore the model with your mouse.

Why the Analysis of Thinking Is Important If you want to think well, you must understand at least the rudiments of thought, the most basic structures out of which all thinking is made. You must learn how to take thinking apart. Analyzing the Logic of a Subject When we understand the elements of reasoning, we realize that all subjects, all disciplines, have a fundamental logic defined by the structures of thought embedded within them. Therefore, to lay bare a subject’s most fundamental logic, we should begin with these questions:

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Watch our Event Calendar , which provides an overview of all upcoming conferences and academies hosted by the Foundation for Critical Thinking. Clicking an entry on the Event Calendar will bring up that event's details, and the option to register. For those interested in online learning, the Foundation offers accredited online courses in critical thinking for both educators and the general public, as well as an online test for evaluating basic comprehension of critical thinking concepts . We are in the process of developing more online learning tools and tests to offer the community.  

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Development of Critical Thinking Skills Through Science Learning

• First Online: 02 January 2023

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• Johar Maknun 3 , 4  

Part of the book series: Integrated Science ((IS,volume 13))

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Education needs to be directed towards increasing the nation’s competitiveness to handle the global competition. This will be achieved as long as learning is directed towards improving students’ abilities, especially critical thinking skills. Therefore, science education aims to improve thinking skills and prepare students for future success. These abilities are trained with learning that requires them to conduct experiments, discover, and solve problems through small group discussions. A constructive learning approach is believed to facilitate and develop these skills.

Graphical Abstract/Art Performance

Development of critical thinking skills through science learning.

(Adapted with permission from the Association of Science and Art(ASA), Universal Scientific Education and Research Network (USERN); Made by Sara Bakhshi)

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Carin AA, Sund RB (1990) Teaching modern science. Merril Publishing Company, New York

Google Scholar  

Carin AA (1997) Teaching science through discovery, 8th edn. Prentice Hall, New Jersey

Morgan WR (1995) Critical thinking what does that mean? J Coll Sci Teach 24(5):336–390

Arnyana IBP (2004) Pengembangan Perangkat Model Belajar Berdasarkan Masalah Dipandu Strategi Kooperatif dan Pengaruh Implementasinya Terhadap Keterampilan Berpikir Kritis dan Hasil Belajar Siswa SMA pada pelajaran Ekosistem. Disertasi tidak diterbitkan. Malang: Program Pasca-sarjana Universitas Negeri Malang

Zoller U, Ben-Caim D, Ron S (2000) The disposition toward critical thinking of high school and university science student: a inter-intra Isreali-Italian study. Int J Sci Educ 22(6).

Costa AL (1985) Goals for a critical thinking curriculum. In: Costa AL (ed) Developing mind: a resource book for teaching thinking. ASCD: Alexandria, Virginia

Ennis RH (1985) Curriculum for critical thinking. In: Costa AL (ed). Developing mind: a resource book for teaching thinking. ASCD: Alexandria, Virginia

Hughes W, Lavery J (2014) Critical thinking: an introduction to the basic skills-seventh edition, Canadian: Phil-papers

Verlinden J (2005) Critical thinking and every day argumen. Balmont. Wadsworth/the msou learning Inc., CA

Facione PA (2015) Critical thinking: what it is and why it counts. Hermosa beach: measured reasons LLC

Abd-El Khalick F, Lederman NG, Bell RL (1998) The nature of science and instructional practice: making the unnatural natural. Sci Educ 82: 417–436

Chiappetta EL, dan Koballa TR (2010) Science instruction in the middle and secondary schools: developing fundamental knowledge and skills. United State of America: Pearson Education Inc

McComas WF (2015) The nature of science & the next generation of biology education. Am Biol Teach 77(7):485–491. https://doi.org/10.1525/abt.2015.77.7.2

Article   Google Scholar  

Bell RL (2008) Best practices in science education teaching the nature of science: three critical questions. Cengage

Good R (2012) Advances in nature of science research, (Id), 97–106. https://doi.org/10.1007/978-94-007-2457-0

Ramsey J (1993) Reform movement implication social responsibility. Sci Educ 77(2):235–258

Loucks-Horsley S, Stiles KE, Mundry S, Love N, Hewson PH (2010) Designing professional development for teacher of science and mathematics. California: Corwin

Brickman P, Gormally C, Armstrong N, Hallar B (2009) Effects of inquiry-based learning on students’ science literacy skills and confidence. Int J Scholarsh Teach Learn 3:1–22

Kruse D (2009) Thinking strategies for the inquiry classroom. Curriculum corporation. Diambil pada tanggal 23 Juli 2014, dari. http://www.curriculumpress.edu.au/sample/pages/978174200313 9.pdf

Beyer BK (1995) Critical thinking. Phi Delta Kappa Educational Foundation, Bloomington

Kalman CS (2002) Developing critical thinking in undergraduate courses: a philosophical approach. Sci Educ 11(83–94):2002

Walker GH (1998) Critical thinking. Tersedia pada: http://www/utr.edu/administration/walkerteachingresoursecenter/facultydevelopment/criticalthinking

Ennis RH (1989) Critical thinking and subject specificity: clarification and needed research. Educ Res 18(3):4–10. https://doi.org/10.3102/0013189X018003004

Tiruneh DT Cock MD, Weldeslassie AG, Elen J, dan Janssen R (2017) Measuring critical thinking in physics: development and validation of a critical thinking test in electricity and magnetism. Int J of Sci and Math Educ 15:663–682

Zohar A (1994) The effect of biology critical thinking project in the development of critical thinking. J Res Sci Teach 31(2):163–196

Tiruneh DT, Weldeslassie AG, Kassa A, Tefera Z, Cock MD, Elen J (2016) Systematic design of a learning environment for domainspecific and domain-general critical thinking skills. Educ Tech Res Dev 2016(64):481–505

Abrami PC, Bernard RM, Borokhovski E, Waddington DI, Wade CA, Persson T (2015) Strategies for teaching students to think critically: a meta-analysis. Rev Educ Res 85(2):275–314. https://doi.org/10.3102/0034654314551063

Hashemi SA, Naderi E, Shariatmadari A, Naraghi MS, Mehrabi M (2010) Science production in iranian educational system by the use of critical thinking. Int J Instr 3(1)

Facione PA (2010) Critical thinking: what it is and why it counts. Insight Assesment, pp 1–24

Deming JC, Cracolice MS (2004) Learning how to think. Sci Teach 71(3):42–47

Straits WJ, Wilke RR (2002) Practical considerations for assessing inquiry-based instruction. J Coll Sci Teach 31(7):432–435

Jufri AW (2007) Pengaruh Implementasi Perangkat Pembelajaran Berbasis Inkuiri Melalui Strategi Kooperatif Terhadap Keterampilan Berpikir Kritis, Sikap, dan penguasaan konsep Siswa SMA di Kota Mataram. Disertasi , tidak dipublikasikan. Malang: Program Pascasarjana Universitas Negeri Malang

Mestre JP (2002) Probing adults’ conceptual understanding and transfer of learning via problem posing. J Appl Dev Psychol 23:9–50

Maknun J (2020) Implementation of guided inquiry learning model to improve understanding physics concepts and critical thinking skill of vocational high school students. Int Educ Stud 13(6). https://doi.org/10.5539/ies.v13n6p117

Peter EE (2012) Critical thinking: essence for teaching mathematics and mathematics problem solving skills. African J Math Comput Sci Res 5(3):39–43. https://doi.org/10.5897/AJMCSR11.161

Stobaugh R (2013) Assessing critical thinking in middle and high schools. Routledge, New York

Book   Google Scholar  

Rosen Y, Maryam T (2014) Making student thinking visible through a concept map in computer-based assessment of critical thinking. J Educ Comput Res 50(2):249–270. https://doi.org/10.2190/EC.50.2.f

Hakim L (2010) Pembelajaran Konsep Listrik Dinamis Melalui Kegiatan Laboratorium Problem Solving untuk Meningkatkan Penguasaan Konsep dan Keterampilan Berpikir Kritis Siswa. Sekolah Pascasarjana Universitas Pendidikan Indonesia, Tesis

Heller, Heller (1999) Cooperative group problem solving in physics, supported by national science foundation (NSF), the department of education, fund for improving post-secondary education (FIPSE), and by the University of Minnesota

Iselin JP (2007) Teaching critical thinking through simple experiment. In: proceeding of the 2007 ASEE North Midwest Sectional Conference

Cabera GA (1991) A framwork for evaluation the teaching of critical thinking. Education 113(1):59–63

Lipman M (2003) A thinking in education, 2nd edn. Cambridge University Press, Cambridge

Shakirova DM (2007) Technology for the shaping of college students’ and upper-grade students’ critical thinking. Russ Educ Soc 49(9):42–52

Maknun J (2019) The development of critical thinking skills in vocational high school students in Indonesia. Int J Innov Creativity Change 7(12):2019

Rosengrant D, Etkina E, Heuvelen AV (2007) An overview of recent research on multiple representations. [online] Tersedia: www.percentral.org/items/264 [18 Jan 2013]

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Maknun, J. (2022). Development of Critical Thinking Skills Through Science Learning. In: Rezaei, N. (eds) Integrated Education and Learning. Integrated Science, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-031-15963-3_8

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3. Critical Thinking in Science: How to Foster Scientific Reasoning Skills

Critical thinking in science is important largely because a lot of students have developed expectations about science that can prove to be counter-productive. 

After various experiences — both in school and out — students often perceive science to be primarily about learning “authoritative” content knowledge: this is how the solar system works; that is how diffusion works; this is the right answer and that is not. 

This perception allows little room for critical thinking in science, in spite of the fact that argument, reasoning, and critical thinking lie at the very core of scientific practice.

Argument, reasoning, and critical thinking lie at the very core of scientific practice.

In this article, we outline two of the best approaches to be most effective in fostering scientific reasoning. Both try to put students in a scientist’s frame of mind more than is typical in science education:

• First, we look at  small-group inquiry , where students formulate questions and investigate them in small groups. This approach is geared more toward younger students but has applications at higher levels too.
• We also look  science   labs . Too often, science labs too often involve students simply following recipes or replicating standard results. Here, we offer tips to turn labs into spaces for independent inquiry and scientific reasoning.

I. Critical Thinking in Science and Scientific Inquiry

Even very young students can “think scientifically” under the right instructional support. A series of experiments , for instance, established that preschoolers can make statistically valid inferences about unknown variables. Through observation they are also capable of distinguishing actions that cause certain outcomes from actions that don’t. These innate capacities, however, have to be developed for students to grow up into rigorous scientific critical thinkers. 

Even very young students can “think scientifically” under the right instructional support.

Although there are many techniques to get young children involved in scientific inquiry — encouraging them to ask and answer “why” questions, for instance — teachers can provide structured scientific inquiry experiences that are deeper than students can experience on their own. 

Goals for Teaching Critical Thinking Through Scientific Inquiry

When it comes to teaching critical thinking via science, the learning goals may vary, but students should learn that:

• Failure to agree is okay, as long as you have reasons for why you disagree about something.
• The logic of scientific inquiry is iterative. Scientists always have to consider how they might improve your methods next time. This includes addressing sources of uncertainty.
• Claims to knowledge usually require multiple lines of evidence and a “match” or “fit” between our explanations and the evidence we have.
• Collaboration, argument, and discussion are central features of scientific reasoning.
• Visualization, analysis, and presentation are central features of scientific reasoning.
• Overarching concepts in scientific practice — such as uncertainty, measurement, and meaningful experimental contrasts — manifest themselves somewhat differently in different scientific domains.

How to Teaching Critical Thinking in Science Via Inquiry

Sometimes we think of science education as being either a “direct” approach, where we tell students about a concept, or an “inquiry-based” approach, where students explore a concept themselves.  

But, especially, at the earliest grades, integrating both approaches can inform students of their options (i.e., generate and extend their ideas), while also letting students make decisions about what to do.

Like a lot of projects targeting critical thinking, limited classroom time is a challenge. Although the latest content standards, such as the Next Generation Science Standards , emphasize teaching scientific practices, many standardized tests still emphasize assessing scientific content knowledge.

The concept of uncertainty comes up in every scientific domain.

Creating a lesson that targets the right content is also an important aspect of developing authentic scientific experiences. It’s now more  widely acknowledged  that effective science instruction involves the interaction between domain-specific knowledge and domain-general knowledge, and that linking an inquiry experience to appropriate target content is vital.

For instance, the concept of uncertainty  comes up  in every scientific domain. But the sources of uncertainty coming from any given measurement vary tremendously by discipline. It requires content knowledge to know how to wisely apply the concept of uncertainty.

Tips and Challenges for teaching critical thinking in science

Teachers need to grapple with student misconceptions. Student intuition about how the world works — the way living things grow and behave, the way that objects fall and interact — often conflicts with scientific explanations. As part of the inquiry experience, teachers can help students to articulate these intuitions and revise them through argument and evidence.

Group composition is another challenge. Teachers will want to avoid situations where one member of the group will simply “take charge” of the decision-making, while other member(s) disengage. In some cases, grouping students by current ability level can make the group work more productive. 

Another approach is to establish group norms that help prevent unproductive group interactions. A third tactic is to have each group member learn an essential piece of the puzzle prior to the group work, so that each member is bringing something valuable to the table (which other group members don’t yet know).

It’s critical to ask students about how certain they are in their observations and explanations and what they could do better next time. When disagreements arise about what to do next or how to interpret evidence, the instructor should model good scientific practice by, for instance, getting students to think about what kind of evidence would help resolve the disagreement or whether there’s a compromise that might satisfy both groups.

The subjects of the inquiry experience and the tools at students’ disposal will depend upon the class and the grade level. Older students may be asked to create mathematical models, more sophisticated visualizations, and give fuller presentations of their results.

Lesson Plan Outline

This lesson plan takes a small-group inquiry approach to critical thinking in science. It asks students to collaboratively explore a scientific question, or perhaps a series of related questions, within a scientific domain.

Suppose students are exploring insect behavior. Groups may decide what questions to ask about insect behavior; how to observe, define, and record insect behavior; how to design an experiment that generates evidence related to their research questions; and how to interpret and present their results.

An in-depth inquiry experience usually takes place over the course of several classroom sessions, and includes classroom-wide instruction, small-group work, and potentially some individual work as well.

Students, especially younger students, will typically need some background knowledge that can inform more independent decision-making. So providing classroom-wide instruction and discussion before individual group work is a good idea.

For instance, Kathleen Metz had students observe insect behavior, explore the anatomy of insects, draw habitat maps, and collaboratively formulate (and categorize) research questions before students began to work more independently.

The subjects of a science inquiry experience can vary tremendously: local weather patterns, plant growth, pollution, bridge-building. The point is to engage students in multiple aspects of scientific practice: observing, formulating research questions, making predictions, gathering data, analyzing and interpreting data, refining and iterating the process.

As student groups take responsibility for their own investigation, teachers act as facilitators. They can circulate around the room, providing advice and guidance to individual groups. If classroom-wide misconceptions arise, they can pause group work to address those misconceptions directly and re-orient the class toward a more productive way of thinking.

Throughout the process, teachers can also ask questions like:

• What are your assumptions about what’s going on? How can you check your assumptions?
• Suppose that your results show X, what would you conclude?
• If you had to do the process over again, what would you change? Why?

II. Rethinking Science Labs

Beyond changing how students approach scientific inquiry, we also need to rethink science labs. After all, science lab activities are ubiquitous in science classrooms and they are a great opportunity to teach critical thinking skills.

Often, however, science labs are merely recipes that students follow to verify standard values (such as the force of acceleration due to gravity) or relationships between variables (such as the relationship between force, mass, and acceleration) known to the students beforehand. 

This approach does not usually involve critical thinking: students are not making many decisions during the process, and they do not reflect on what they’ve done except to see whether their experimental data matches the expected values.

With some small tweaks, however, science labs can involve more critical thinking. Science lab activities that give students not only the opportunity to design, analyze, and interpret the experiment, but re -design, re -analyze, and re -interpret the experiment provides ample opportunity for grappling with evidence and evidence-model relationships, particularly if students don’t know what answer they should be expecting beforehand.

Such activities improve scientific reasoning skills, such as: 

• Evaluating quantitative data
• Plausible scientific explanations for observed patterns

And also broader critical thinking skills, like:

• Comparing models to data, and comparing models to each other
• Thinking about what kind of evidence supports one model or another
• Being open to changing your beliefs based on evidence

Traditional science lab experiences bear little resemblance to actual scientific practice. Actual practice  involves  decision-making under uncertainty, trial-and-error, tweaking experimental methods over time, testing instruments, and resolving conflicts among different kinds of evidence. Traditional in-school science labs rarely involve these things.

Traditional science lab experiences bear little resemblance to actual scientific practice.

When teachers use science labs as opportunities to engage students in the kinds of dilemmas that scientists actually face during research, students make more decisions and exhibit more sophisticated reasoning.

In the lesson plan below, students are asked to evaluate two models of drag forces on a falling object. One model assumes that drag increases linearly with the velocity of the falling object. Another model assumes that drag increases quadratically (e.g., with the square of the velocity).  Students use a motion detector and computer software to create a plot of the position of a disposable paper coffee filter as it falls to the ground. Among other variables, students can vary the number of coffee filters they drop at once, the height at which they drop them, how they drop  them, and how they clean their data. This is an approach to scaffolding critical thinking: a way to get students to ask the right kinds of questions and think in the way that scientists tend to think.

Design an experiment to test which model best characterizes the motion of the coffee filters. 

Things to think about in your design:

• What are the relevant variables to control and which ones do you need to explore?
• What are some logistical issues associated with the data collection that may cause unnecessary variability (either random or systematic) or mistakes?
• How can you control or measure these?
• What ways can you graph your data and which ones will help you figure out which model better describes your data?

Discuss your design with other groups and modify as you see fit.

Initial data collection

Conduct a quick trial-run of your experiment so that you can evaluate your methods.

• Do your graphs provide evidence of which model is the best?
• What ways can you improve your methods, data, or graphs to make your case more convincing?
• Do you need to change how you’re collecting data?
• Do you need to take data at different regions?
• Do you just need more data?
• Do you need to reduce your uncertainty?

After this initial evaluation of your data and methods, conduct the desired improvements, changes, or additions and re-evaluate at the end.

In your lab notes, make sure to keep track of your progress and process as you go. As always, your final product is less important than how you get there.

How to Make Science Labs Run Smoothly

Managing student expectations . As with many other lesson plans that incorporate critical thinking, students are not used to having so much freedom. As with the example lesson plan above, it’s important to scaffold student decision-making by pointing out what decisions have to be made, especially as students are transitioning to this approach.

Supporting student reasoning . Another challenge is to provide guidance to student groups without telling them how to do something. Too much “telling” diminishes student decision-making, but not enough support may leave students simply not knowing what to do. 

There are several key strategies teachers can try out here: 

• Point out an issue with their data collection process without specifying exactly how to solve it.
• Ask a lab group how they would improve their approach.
• Ask two groups with conflicting results to compare their results, methods, and analyses.

Download our Teachers’ Guide

(please click here)

Sources and Resources

Lehrer, R., & Schauble, L. (2007). Scientific thinking and scientific literacy . Handbook of child psychology , Vol. 4. Wiley. A review of research on scientific thinking and experiments on teaching scientific thinking in the classroom.

Metz, K. (2004). Children’s understanding of scientific inquiry: Their conceptualizations of uncertainty in investigations of their own design . Cognition and Instruction 22(2). An example of a scientific inquiry experience for elementary school students.

The Next Generation Science Standards . The latest U.S. science content standards.

Concepts of Evidence A collection of important concepts related to evidence that cut across scientific disciplines.

Scienceblind A book about children’s science misconceptions and how to correct them.

Holmes, N. G., Keep, B., & Wieman, C. E. (2020). Developing scientific decision making by structuring and supporting student agency. Physical Review Physics Education Research , 16 (1), 010109. A research study on minimally altering traditional lab approaches to incorporate more critical thinking. The drag example was taken from this piece.

ISLE , led by E. Etkina.  A platform that helps teachers incorporate more critical thinking in physics labs.

Holmes, N. G., Wieman, C. E., & Bonn, D. A. (2015). Teaching critical thinking . Proceedings of the National Academy of Sciences , 112 (36), 11199-11204. An approach to improving critical thinking and reflection in science labs. Walker, J. P., Sampson, V., Grooms, J., Anderson, B., & Zimmerman, C. O. (2012). Argument-driven inquiry in undergraduate chemistry labs: The impact on students’ conceptual understanding, argument skills, and attitudes toward science . Journal of College Science Teaching , 41 (4), 74-81. A large-scale research study on transforming chemistry labs to be more inquiry-based.

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35 Scientific Thinking and Reasoning

Kevin N. Dunbar, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD

David Klahr, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA

• Published: 21 November 2012
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Scientific thinking refers to both thinking about the content of science and the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. Here we cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research. Future research will focus on the collaborative aspects of scientific thinking, on effective methods for teaching science, and on the neural underpinnings of the scientific mind.

There is no unitary activity called “scientific discovery”; there are activities of designing experiments, gathering data, inventing and developing observational instruments, formulating and modifying theories, deducing consequences from theories, making predictions from theories, testing theories, inducing regularities and invariants from data, discovering theoretical constructs, and others. — Simon, Langley, & Bradshaw, 1981 , p. 2

What Is Scientific Thinking and Reasoning?

There are two kinds of thinking we call “scientific.” The first, and most obvious, is thinking about the content of science. People are engaged in scientific thinking when they are reasoning about such entities and processes as force, mass, energy, equilibrium, magnetism, atoms, photosynthesis, radiation, geology, or astrophysics (and, of course, cognitive psychology!). The second kind of scientific thinking includes the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. However, these reasoning processes are not unique to scientific thinking: They are the very same processes involved in everyday thinking. As Einstein put it:

The scientific way of forming concepts differs from that which we use in our daily life, not basically, but merely in the more precise definition of concepts and conclusions; more painstaking and systematic choice of experimental material, and greater logical economy. (The Common Language of Science, 1941, reprinted in Einstein, 1950 , p. 98)

Nearly 40 years after Einstein's remarkably insightful statement, Francis Crick offered a similar perspective: that great discoveries in science result not from extraordinary mental processes, but rather from rather common ones. The greatness of the discovery lies in the thing discovered.

I think what needs to be emphasized about the discovery of the double helix is that the path to it was, scientifically speaking, fairly commonplace. What was important was not the way it was discovered , but the object discovered—the structure of DNA itself. (Crick, 1988 , p. 67; emphasis added)

Under this view, scientific thinking involves the same general-purpose cognitive processes—such as induction, deduction, analogy, problem solving, and causal reasoning—that humans apply in nonscientific domains. These processes are covered in several different chapters of this handbook: Rips, Smith, & Medin, Chapter 11 on induction; Evans, Chapter 8 on deduction; Holyoak, Chapter 13 on analogy; Bassok & Novick, Chapter 21 on problem solving; and Cheng & Buehner, Chapter 12 on causality. One might question the claim that the highly specialized procedures associated with doing science in the “real world” can be understood by investigating the thinking processes used in laboratory studies of the sort described in this volume. However, when the focus is on major scientific breakthroughs, rather than on the more routine, incremental progress in a field, the psychology of problem solving provides a rich source of ideas about how such discoveries might occur. As Simon and his colleagues put it:

It is understandable, if ironic, that ‘normal’ science fits … the description of expert problem solving, while ‘revolutionary’ science fits the description of problem solving by novices. It is understandable because scientific activity, particularly at the revolutionary end of the continuum, is concerned with the discovery of new truths, not with the application of truths that are already well-known … it is basically a journey into unmapped terrain. Consequently, it is mainly characterized, as is novice problem solving, by trial-and-error search. The search may be highly selective—but it reaches its goal only after many halts, turnings, and back-trackings. (Simon, Langley, & Bradshaw, 1981 , p. 5)

The research literature on scientific thinking can be roughly categorized according to the two types of scientific thinking listed in the opening paragraph of this chapter: (1) One category focuses on thinking that directly involves scientific content . Such research ranges from studies of young children reasoning about the sun-moon-earth system (Vosniadou & Brewer, 1992 ) to college students reasoning about chemical equilibrium (Davenport, Yaron, Klahr, & Koedinger, 2008 ), to research that investigates collaborative problem solving by world-class researchers in real-world molecular biology labs (Dunbar, 1995 ). (2) The other category focuses on “general” cognitive processes, but it tends to do so by analyzing people's problem-solving behavior when they are presented with relatively complex situations that involve the integration and coordination of several different types of processes, and that are designed to capture some essential features of “real-world” science in the psychology laboratory (Bruner, Goodnow, & Austin, 1956 ; Klahr & Dunbar, 1988 ; Mynatt, Doherty, & Tweney, 1977 ).

There are a number of overlapping research traditions that have been used to investigate scientific thinking. We will cover both the history of research on scientific thinking and the different approaches that have been used, highlighting common themes that have emerged over the past 50 years of research.

A Brief History of Research on Scientific Thinking

Science is often considered one of the hallmarks of the human species, along with art and literature. Illuminating the thought processes used in science thus reveal key aspects of the human mind. The thought processes underlying scientific thinking have fascinated both scientists and nonscientists because the products of science have transformed our world and because the process of discovery is shrouded in mystery. Scientists talk of the chance discovery, the flash of insight, the years of perspiration, and the voyage of discovery. These images of science have helped make the mental processes underlying the discovery process intriguing to cognitive scientists as they attempt to uncover what really goes on inside the scientific mind and how scientists really think. Furthermore, the possibilities that scientists can be taught to think better by avoiding mistakes that have been clearly identified in research on scientific thinking, and that their scientific process could be partially automated, makes scientific thinking a topic of enduring interest.

The cognitive processes underlying scientific discovery and day-to-day scientific thinking have been a topic of intense scrutiny and speculation for almost 400 years (e.g., Bacon, 1620 ; Galilei 1638 ; Klahr 2000 ; Tweney, Doherty, & Mynatt, 1981 ). Understanding the nature of scientific thinking has been a central issue not only for our understanding of science but also for our understating of what it is to be human. Bacon's Novumm Organum in 1620 sketched out some of the key features of the ways that experiments are designed and data interpreted. Over the ensuing 400 years philosophers and scientists vigorously debated about the appropriate methods that scientists should use (see Giere, 1993 ). These debates over the appropriate methods for science typically resulted in the espousal of a particular type of reasoning method, such as induction or deduction. It was not until the Gestalt psychologists began working on the nature of human problem solving, during the 1940s, that experimental psychologists began to investigate the cognitive processes underlying scientific thinking and reasoning.

The Gestalt psychologist Max Wertheimer pioneered the investigation of scientific thinking (of the first type described earlier: thinking about scientific content ) in his landmark book Productive Thinking (Wertheimer, 1945 ). Wertheimer spent a considerable amount of time corresponding with Albert Einstein, attempting to discover how Einstein generated the concept of relativity. Wertheimer argued that Einstein had to overcome the structure of Newtonian physics at each step in his theorizing, and the ways that Einstein actually achieved this restructuring were articulated in terms of Gestalt theories. (For a recent and different account of how Einstein made his discovery, see Galison, 2003 .) We will see later how this process of overcoming alternative theories is an obstacle that both scientists and nonscientists need to deal with when evaluating and theorizing about the world.

One of the first investigations of scientific thinking of the second type (i.e., collections of general-purpose processes operating on complex, abstract, components of scientific thought) was carried out by Jerome Bruner and his colleagues at Harvard (Bruner et al., 1956 ). They argued that a key activity engaged in by scientists is to determine whether a particular instance is a member of a category. For example, a scientist might want to discover which substances undergo fission when bombarded by neutrons and which substances do not. Here, scientists have to discover the attributes that make a substance undergo fission. Bruner et al. saw scientific thinking as the testing of hypotheses and the collecting of data with the end goal of determining whether something is a member of a category. They invented a paradigm where people were required to formulate hypotheses and collect data that test their hypotheses. In one type of experiment, the participants were shown a card such as one with two borders and three green triangles. The participants were asked to determine the concept that this card represented by choosing other cards and getting feedback from the experimenter as to whether the chosen card was an example of the concept. In this case the participant may have thought that the concept was green and chosen a card with two green squares and one border. If the underlying concept was green, then the experimenter would say that the card was an example of the concept. In terms of scientific thinking, choosing a new card is akin to conducting an experiment, and the feedback from the experimenter is similar to knowing whether a hypothesis is confirmed or disconfirmed. Using this approach, Bruner et al. identified a number of strategies that people use to formulate and test hypotheses. They found that a key factor determining which hypothesis-testing strategy that people use is the amount of memory capacity that the strategy takes up (see also Morrison & Knowlton, Chapter 6 ; Medin et al., Chapter 11 ). Another key factor that they discovered was that it was much more difficult for people to discover negative concepts (e.g., not blue) than positive concepts (e.g., blue). Although Bruner et al.'s research is most commonly viewed as work on concepts, they saw their work as uncovering a key component of scientific thinking.

A second early line of research on scientific thinking was developed by Peter Wason and his colleagues (Wason, 1968 ). Like Bruner et al., Wason saw a key component of scientific thinking as being the testing of hypotheses. Whereas Bruner et al. focused on the different types of strategies that people use to formulate hypotheses, Wason focused on whether people adopt a strategy of trying to confirm or disconfirm their hypotheses. Using Popper's ( 1959 ) theory that scientists should try and falsify rather than confirm their hypotheses, Wason devised a deceptively simple task in which participants were given three numbers, such as 2-4-6, and were asked to discover the rule underlying the three numbers. Participants were asked to generate other triads of numbers and the experimenter would tell the participant whether the triad was consistent or inconsistent with the rule. They were told that when they were sure they knew what the rule was they should state it. Most participants began the experiment by thinking that the rule was even numbers increasing by 2. They then attempted to confirm their hypothesis by generating a triad like 8-10-12, then 14-16-18. These triads are consistent with the rule and the participants were told yes, that the triads were indeed consistent with the rule. However, when they proposed the rule—even numbers increasing by 2—they were told that the rule was incorrect. The correct rule was numbers of increasing magnitude! From this research, Wason concluded that people try to confirm their hypotheses, whereas normatively speaking, they should try to disconfirm their hypotheses. One implication of this research is that confirmation bias is not just restricted to scientists but is a general human tendency.

It was not until the 1970s that a general account of scientific reasoning was proposed. Herbert Simon, often in collaboration with Allan Newell, proposed that scientific thinking is a form of problem solving. He proposed that problem solving is a search in a problem space. Newell and Simon's theory of problem solving is discussed in many places in this handbook, usually in the context of specific problems (see especially Bassok & Novick, Chapter 21 ). Herbert Simon, however, devoted considerable time to understanding many different scientific discoveries and scientific reasoning processes. The common thread in his research was that scientific thinking and discovery is not a mysterious magical process but a process of problem solving in which clear heuristics are used. Simon's goal was to articulate the heuristics that scientists use in their research at a fine-grained level. By constructing computer programs that simulated the process of several major scientific discoveries, Simon and colleagues were able to articulate the specific computations that scientists could have used in making those discoveries (Langley, Simon, Bradshaw, & Zytkow, 1987 ; see section on “Computational Approaches to Scientific Thinking”). Particularly influential was Simon and Lea's ( 1974 ) work demonstrating that concept formation and induction consist of a search in two problem spaces: a space of instances and a space of rules. This idea has influenced problem-solving accounts of scientific thinking that will be discussed in the next section.

Overall, the work of Bruner, Wason, and Simon laid the foundations for contemporary research on scientific thinking. Early research on scientific thinking is summarized in Tweney, Doherty and Mynatt's 1981 book On Scientific Thinking , where they sketched out many of the themes that have dominated research on scientific thinking over the past few decades. Other more recent books such as Cognitive Models of Science (Giere, 1993 ), Exploring Science (Klahr, 2000 ), Cognitive Basis of Science (Carruthers, Stich, & Siegal, 2002 ), and New Directions in Scientific and Technical Thinking (Gorman, Kincannon, Gooding, & Tweney, 2004 ) provide detailed analyses of different aspects of scientific discovery. Another important collection is Vosnadiau's handbook on conceptual change research (Vosniadou, 2008 ). In this chapter, we discuss the main approaches that have been used to investigate scientific thinking.

How does one go about investigating the many different aspects of scientific thinking? One common approach to the study of the scientific mind has been to investigate several key aspects of scientific thinking using abstract tasks designed to mimic some essential characteristics of “real-world” science. There have been numerous methodologies that have been used to analyze the genesis of scientific concepts, theories, hypotheses, and experiments. Researchers have used experiments, verbal protocols, computer programs, and analyzed particular scientific discoveries. A more recent development has been to increase the ecological validity of such research by investigating scientists as they reason “live” (in vivo studies of scientific thinking) in their own laboratories (Dunbar, 1995 , 2002 ). From a “Thinking and Reasoning” standpoint the major aspects of scientific thinking that have been most actively investigated are problem solving, analogical reasoning, hypothesis testing, conceptual change, collaborative reasoning, inductive reasoning, and deductive reasoning.

Scientific Thinking as Problem Solving

One of the primary goals of accounts of scientific thinking has been to provide an overarching framework to understand the scientific mind. One framework that has had a great influence in cognitive science is that scientific thinking and scientific discovery can be conceived as a form of problem solving. As noted in the opening section of this chapter, Simon ( 1977 ; Simon, Langley, & Bradshaw, 1981 ) argued that both scientific thinking in general and problem solving in particular could be thought of as a search in a problem space. A problem space consists of all the possible states of a problem and all the operations that a problem solver can use to get from one state to the next. According to this view, by characterizing the types of representations and procedures that people use to get from one state to another it is possible to understand scientific thinking. Thus, scientific thinking can be characterized as a search in various problem spaces (Simon, 1977 ). Simon investigated a number of scientific discoveries by bringing participants into the laboratory, providing the participants with the data that a scientist had access to, and getting the participants to reason about the data and rediscover a scientific concept. He then analyzed the verbal protocols that participants generated and mapped out the types of problem spaces that the participants search in (e.g., Qin & Simon, 1990 ). Kulkarni and Simon ( 1988 ) used a more historical approach to uncover the problem-solving heuristics that Krebs used in his discovery of the urea cycle. Kulkarni and Simon analyzed Krebs's diaries and proposed a set of problem-solving heuristics that he used in his research. They then built a computer program incorporating the heuristics and biological knowledge that Krebs had before he made his discoveries. Of particular importance are the search heuristics that the program uses, which include experimental proposal heuristics and data interpretation heuristics. A key heuristic was an unusualness heuristic that focused on unusual findings, which guided search through a space of theories and a space of experiments.

Klahr and Dunbar ( 1988 ) extended the search in a problem space approach and proposed that scientific thinking can be thought of as a search through two related spaces: an hypothesis space and an experiment space. Each problem space that a scientist uses will have its own types of representations and operators used to change the representations. Search in the hypothesis space constrains search in the experiment space. Klahr and Dunbar found that some participants move from the hypothesis space to the experiment space, whereas others move from the experiment space to the hypothesis space. These different types of searches lead to the proposal of different types of hypotheses and experiments. More recent work has extended the dual-space approach to include alternative problem-solving spaces, including those for data, instrumentation, and domain-specific knowledge (Klahr & Simon, 1999 ; Schunn & Klahr, 1995 , 1996 ).

Scientific Thinking as Hypothesis Testing

Many researchers have regarded testing specific hypotheses predicted by theories as one of the key attributes of scientific thinking. Hypothesis testing is the process of evaluating a proposition by collecting evidence regarding its truth. Experimental cognitive research on scientific thinking that specifically examines this issue has tended to fall into two broad classes of investigations. The first class is concerned with the types of reasoning that lead scientists astray, thus blocking scientific ingenuity. A large amount of research has been conducted on the potentially faulty reasoning strategies that both participants in experiments and scientists use, such as considering only one favored hypothesis at a time and how this prevents the scientists from making discoveries. The second class is concerned with uncovering the mental processes underlying the generation of new scientific hypotheses and concepts. This research has tended to focus on the use of analogy and imagery in science, as well as the use of specific types of problem-solving heuristics.

Turning first to investigations of what diminishes scientific creativity, philosophers, historians, and experimental psychologists have devoted a considerable amount of research to “confirmation bias.” This occurs when scientists only consider one hypothesis (typically the favored hypothesis) and ignore other alternative hypotheses or potentially relevant hypotheses. This important phenomenon can distort the design of experiments, formulation of theories, and interpretation of data. Beginning with the work of Wason ( 1968 ) and as discussed earlier, researchers have repeatedly shown that when participants are asked to design an experiment to test a hypothesis they will predominantly design experiments that they think will yield results consistent with the hypothesis. Using the 2-4-6 task mentioned earlier, Klayman and Ha ( 1987 ) showed that in situations where one's hypothesis is likely to be confirmed, seeking confirmation is a normatively incorrect strategy, whereas when the probability of confirming one's hypothesis is low, then attempting to confirm one's hypothesis can be an appropriate strategy. Historical analyses by Tweney ( 1989 ), concerning the way that Faraday made his discoveries, and experiments investigating people testing hypotheses, have revealed that people use a confirm early, disconfirm late strategy: When people initially generate or are given hypotheses, they try and gather evidence that is consistent with the hypothesis. Once enough evidence has been gathered, then people attempt to find the boundaries of their hypothesis and often try to disconfirm their hypotheses.

In an interesting variant on the confirmation bias paradigm, Gorman ( 1989 ) showed that when participants are told that there is the possibility of error in the data that they receive, participants assume that any data that are inconsistent with their favored hypothesis are due to error. Thus, the possibility of error “insulates” hypotheses against disconfirmation. This intriguing hypothesis has not been confirmed by other researchers (Penner & Klahr, 1996 ), but it is an intriguing hypothesis that warrants further investigation.

Confirmation bias is very difficult to overcome. Even when participants are asked to consider alternate hypotheses, they will often fail to conduct experiments that could potentially disconfirm their hypothesis. Tweney and his colleagues provide an excellent overview of this phenomenon in their classic monograph On Scientific Thinking (1981). The precise reasons for this type of block are still widely debated. Researchers such as Michael Doherty have argued that working memory limitations make it difficult for people to consider more than one hypothesis. Consistent with this view, Dunbar and Sussman ( 1995 ) have shown that when participants are asked to hold irrelevant items in working memory while testing hypotheses, the participants will be unable to switch hypotheses in the face of inconsistent evidence. While working memory limitations are involved in the phenomenon of confirmation bias, even groups of scientists can also display confirmation bias. For example, the controversy over cold fusion is an example of confirmation bias. Here, large groups of scientists had other hypotheses available to explain their data yet maintained their hypotheses in the face of other more standard alternative hypotheses. Mitroff ( 1974 ) provides some interesting examples of NASA scientists demonstrating confirmation bias, which highlight the roles of commitment and motivation in this process. See also MacPherson and Stanovich ( 2007 ) for specific strategies that can be used to overcome confirmation bias.

Causal Thinking in Science

Much of scientific thinking and scientific theory building pertains to the development of causal models between variables of interest. For example, do vaccines cause illnesses? Do carbon dioxide emissions cause global warming? Does water on a planet indicate that there is life on the planet? Scientists and nonscientists alike are constantly bombarded with statements regarding the causal relationship between such variables. How does one evaluate the status of such claims? What kinds of data are informative? How do scientists and nonscientists deal with data that are inconsistent with their theory?

A central issue in the causal reasoning literature, one that is directly relevant to scientific thinking, is the extent to which scientists and nonscientists alike are governed by the search for causal mechanisms (i.e., how a variable works) versus the search for statistical data (i.e., how often variables co-occur). This dichotomy can be boiled down to the search for qualitative versus quantitative information about the paradigm the scientist is investigating. Researchers from a number of cognitive psychology laboratories have found that people prefer to gather more information about an underlying mechanism than covariation between a cause and an effect (e.g., Ahn, Kalish, Medin, & Gelman, 1995 ). That is, the predominant strategy that students in simulations of scientific thinking use is to gather as much information as possible about how the objects under investigation work, rather than collecting large amounts of quantitative data to determine whether the observations hold across multiple samples. These findings suggest that a central component of scientific thinking may be to formulate explicit mechanistic causal models of scientific events.

One type of situation in which causal reasoning has been observed extensively is when scientists obtain unexpected findings. Both historical and naturalistic research has revealed that reasoning causally about unexpected findings plays a central role in science. Indeed, scientists themselves frequently state that a finding was due to chance or was unexpected. Given that claims of unexpected findings are such a frequent component of scientists' autobiographies and interviews in the media, Dunbar ( 1995 , 1997 , 1999 ; Dunbar & Fugelsang, 2005 ; Fugelsang, Stein, Green, & Dunbar, 2004 ) decided to investigate the ways that scientists deal with unexpected findings. In 1991–1992 Dunbar spent 1 year in three molecular biology laboratories and one immunology laboratory at a prestigious U.S. university. He used the weekly laboratory meeting as a source of data on scientific discovery and scientific reasoning. (He termed this type of study “in vivo” cognition.) When he looked at the types of findings that the scientists made, he found that over 50% of the findings were unexpected and that these scientists had evolved a number of effective strategies for dealing with such findings. One clear strategy was to reason causally about the findings: Scientists attempted to build causal models of their unexpected findings. This causal model building results in the extensive use of collaborative reasoning, analogical reasoning, and problem-solving heuristics (Dunbar, 1997 , 2001 ).

Many of the key unexpected findings that scientists reasoned about in the in vivo studies of scientific thinking were inconsistent with the scientists' preexisting causal models. A laboratory equivalent of the biology labs involved creating a situation in which students obtained unexpected findings that were inconsistent with their preexisting theories. Dunbar and Fugelsang ( 2005 ) examined this issue by creating a scientific causal thinking simulation where experimental outcomes were either expected or unexpected. Dunbar ( 1995 ) has called the study of people reasoning in a cognitive laboratory “in vitro” cognition. These investigators found that students spent considerably more time reasoning about unexpected findings than expected findings. In addition, when assessing the overall degree to which their hypothesis was supported or refuted, participants spent the majority of their time considering unexpected findings. An analysis of participants' verbal protocols indicates that much of this extra time was spent formulating causal models for the unexpected findings. Similarly, scientists spend more time considering unexpected than expected findings, and this time is devoted to building causal models (Dunbar & Fugelsang, 2004 ).

Scientists know that unexpected findings occur often, and they have developed many strategies to take advantage of their unexpected findings. One of the most important places that they anticipate the unexpected is in designing experiments (Baker & Dunbar, 2000 ). They build different causal models of their experiments incorporating many conditions and controls. These multiple conditions and controls allow unknown mechanisms to manifest themselves. Thus, rather than being the victims of the unexpected, they create opportunities for unexpected events to occur, and once these events do occur, they have causal models that allow them to determine exactly where in the causal chain their unexpected finding arose. The results of these in vivo and in vitro studies all point to a more complex and nuanced account of how scientists and nonscientists alike test and evaluate hypotheses about theories.

The Roles of Inductive, Abductive, and Deductive Thinking in Science

One of the most basic characteristics of science is that scientists assume that the universe that we live in follows predictable rules. Scientists reason using a variety of different strategies to make new scientific discoveries. Three frequently used types of reasoning strategies that scientists use are inductive, abductive, and deductive reasoning. In the case of inductive reasoning, a scientist may observe a series of events and try to discover a rule that governs the event. Once a rule is discovered, scientists can extrapolate from the rule to formulate theories of observed and yet-to-be-observed phenomena. One example is the discovery using inductive reasoning that a certain type of bacterium is a cause of many ulcers (Thagard, 1999 ). In a fascinating series of articles, Thagard documented the reasoning processes that Marshall and Warren went through in proposing this novel hypothesis. One key reasoning process was the use of induction by generalization. Marshall and Warren noted that almost all patients with gastric entritis had a spiral bacterium in their stomachs, and he formed the generalization that this bacterium is the cause of stomach ulcers. There are numerous other examples of induction by generalization in science, such as Tycho De Brea's induction about the motion of planets from his observations, Dalton's use of induction in chemistry, and the discovery of prions as the source of mad cow disease. Many theories of induction have used scientific discovery and reasoning as examples of this important reasoning process.

Another common type of inductive reasoning is to map a feature of one member of a category to another member of a category. This is called categorical induction. This type of induction is a way of projecting a known property of one item onto another item that is from the same category. Thus, knowing that the Rous Sarcoma virus is a retrovirus that uses RNA rather than DNA, a biologist might assume that another virus that is thought to be a retrovirus also uses RNA rather than DNA. While research on this type of induction typically has not been discussed in accounts of scientific thinking, this type of induction is common in science. For an influential contribution to this literature, see Smith, Shafir, and Osherson ( 1993 ), and for reviews of this literature see Heit ( 2000 ) and Medin et al. (Chapter 11 ).

While less commonly mentioned than inductive reasoning, abductive reasoning is an important form of reasoning that scientists use when they are seeking to propose explanations for events such as unexpected findings (see Lombrozo, Chapter 14 ; Magnani, et al., 2010 ). In Figure 35.1 , taken from King ( 2011 ), the differences between inductive, abductive, and deductive thinking are highlighted. In the case of abduction, the reasoner attempts to generate explanations of the form “if situation X had occurred, could it have produced the current evidence I am attempting to interpret?” (For an interesting of analysis of abductive reasoning see the brief paper by Klahr & Masnick, 2001 ). Of course, as in classical induction, such reasoning may produce a plausible account that is still not the correct one. However, abduction does involve the generation of new knowledge, and is thus also related to research on creativity.

The different processes underlying inductive, abductive, and deductive reasoning in science. (Figure reproduced from King 2011 ).)

Turning now to deductive thinking, many thinking processes that scientists adhere to follow traditional rules of deductive logic. These processes correspond to those conditions in which a hypothesis may lead to, or is deducible to, a conclusion. Though they are not always phrased in syllogistic form, deductive arguments can be phrased as “syllogisms,” or as brief, mathematical statements in which the premises lead to the conclusion. Deductive reasoning is an extremely important aspect of scientific thinking because it underlies a large component of how scientists conduct their research. By looking at many scientific discoveries, we can often see that deductive reasoning is at work. Deductive reasoning statements all contain information or rules that state an assumption about how the world works, as well as a conclusion that would necessarily follow from the rule. Numerous discoveries in physics such as the discovery of dark matter by Vera Rubin are based on deductions. In the dark matter case, Rubin measured galactic rotation curves and based on the differences between the predicted and observed angular motions of galaxies she deduced that the structure of the universe was uneven. This led her to propose that dark matter existed. In contemporary physics the CERN Large Hadron Collider is being used to search for the Higgs Boson. The Higgs Boson is a deductive prediction from contemporary physics. If the Higgs Boson is not found, it may lead to a radical revision of the nature of physics and a new understanding of mass (Hecht, 2011 ).

The Roles of Analogy in Scientific Thinking

One of the most widely mentioned reasoning processes used in science is analogy. Scientists use analogies to form a bridge between what they already know and what they are trying to explain, understand, or discover. In fact, many scientists have claimed that the making of certain analogies was instrumental in their making a scientific discovery, and almost all scientific autobiographies and biographies feature one particular analogy that is discussed in depth. Coupled with the fact that there has been an enormous research program on analogical thinking and reasoning (see Holyoak, Chapter 13 ), we now have a number of models and theories of analogical reasoning that suggest how analogy can play a role in scientific discovery (see Gentner, Holyoak, & Kokinov, 2001 ). By analyzing several major discoveries in the history of science, Thagard and Croft ( 1999 ), Nersessian ( 1999 , 2008 ), and Gentner and Jeziorski ( 1993 ) have all shown that analogical reasoning is a key aspect of scientific discovery.

Traditional accounts of analogy distinguish between two components of analogical reasoning: the target and the source (Holyoak, Chapter 13 ; Gentner 2010 ). The target is the concept or problem that a scientist is attempting to explain or solve. The source is another piece of knowledge that the scientist uses to understand the target or to explain the target to others. What the scientist does when he or she makes an analogy is to map features of the source onto features of the target. By mapping the features of the source onto the target, new features of the target may be discovered, or the features of the target may be rearranged so that a new concept is invented and a scientific discovery is made. For example, a common analogy that is used with computers is to describe a harmful piece of software as a computer virus. Once a piece of software is called a virus, people can map features of biological viruses, such as that it is small, spreads easily, self-replicates using a host, and causes damage. People not only map individual features of the source onto the target but also the systems of relations. For example, if a computer virus is similar to a biological virus, then an immune system can be created on computers that can protect computers from future variants of a virus. One of the reasons that scientific analogy is so powerful is that it can generate new knowledge, such as the creation of a computational immune system having many of the features of a real biological immune system. This analogy also leads to predictions that there will be newer computer viruses that are the computational equivalent of retroviruses, lacking DNA, or standard instructions, that will elude the computational immune system.

The process of making an analogy involves a number of key steps: retrieval of a source from memory, aligning the features of the source with those of the target, mapping features of the source onto those of the target, and possibly making new inferences about the target. Scientific discoveries are made when the source highlights a hitherto unknown feature of the target or restructures the target into a new set of relations. Interestingly, research on analogy has shown that participants do not easily use remote analogies (see Gentner et al., 1997 ; Holyoak & Thagard 1995 ). Participants in experiments tend to focus on the sharing of a superficial feature between the source and the target, rather than the relations among features. In his in vivo studies of science, Dunbar ( 1995 , 2001 , 2002 ) investigated the ways that scientists use analogies while they are conducting their research and found that scientists use both relational and superficial features when they make analogies. Whether they use superficial or relational features depends on their goals. If their goal is to fix a problem in an experiment, their analogies are based upon superficial features. However, if their goal is to formulate hypotheses, they focus on analogies based upon sets of relations. One important difference between scientists and participants in experiments is that the scientists have deep relational knowledge of the processes that they are investigating and can hence use this relational knowledge to make analogies (see Holyoak, Chapter 13 for a thorough review of analogical reasoning).

Are scientific analogies always useful? Sometimes analogies can lead scientists and students astray. For example, Evelyn Fox-Keller ( 1985 ) shows how an analogy between the pulsing of a lighthouse and the activity of the slime mold dictyostelium led researchers astray for a number of years. Likewise, the analogy between the solar system (the source) and the structure of the atom (the target) has been shown to be potentially misleading to students taking more advanced courses in physics or chemistry. The solar system analogy has a number of misalignments to the structure of the atom, such as electrons being repelled from each other rather than attracted; moreover, electrons do not have individual orbits like planets but have orbit clouds of electron density. Furthermore, students have serious misconceptions about the nature of the solar system, which can compound their misunderstanding of the nature of the atom (Fischler & Lichtfeld, 1992 ). While analogy is a powerful tool in science, like all forms of induction, incorrect conclusions can be reached.

Conceptual Change in Science

Scientific knowledge continually accumulates as scientists gather evidence about the natural world. Over extended time, this knowledge accumulation leads to major revisions, extensions, and new organizational forms for expressing what is known about nature. Indeed, these changes are so substantial that philosophers of science speak of “revolutions” in a variety of scientific domains (Kuhn, 1962 ). The psychological literature that explores the idea of revolutionary conceptual change can be roughly divided into (a) investigations of how scientists actually make discoveries and integrate those discoveries into existing scientific contexts, and (b) investigations of nonscientists ranging from infants, to children, to students in science classes. In this section we summarize the adult studies of conceptual change, and in the next section we look at its developmental aspects.

Scientific concepts, like all concepts, can be characterized as containing a variety of “knowledge elements”: representations of words, thoughts, actions, objects, and processes. At certain points in the history of science, the accumulated evidence has demanded major shifts in the way these collections of knowledge elements are organized. This “radical conceptual change” process (see Keil, 1999 ; Nersessian 1998 , 2002 ; Thagard, 1992 ; Vosniadou 1998, for reviews) requires the formation of a new conceptual system that organizes knowledge in new ways, adds new knowledge, and results in a very different conceptual structure. For more recent research on conceptual change, The International Handbook of Research on Conceptual Change (Vosniadou, 2008 ) provides a detailed compendium of theories and controversies within the field.

While conceptual change in science is usually characterized by large-scale changes in concepts that occur over extensive periods of time, it has been possible to observe conceptual change using in vivo methodologies. Dunbar ( 1995 ) reported a major conceptual shift that occurred in immunologists, where they obtained a series of unexpected findings that forced the scientists to propose a new concept in immunology that in turn forced the change in other concepts. The drive behind this conceptual change was the discovery of a series of different unexpected findings or anomalies that required the scientists to both revise and reorganize their conceptual knowledge. Interestingly, this conceptual change was achieved by a group of scientists reasoning collaboratively, rather than by a scientist working alone. Different scientists tend to work on different aspects of concepts, and also different concepts, that when put together lead to a rapid change in entire conceptual structures.

Overall, accounts of conceptual change in individuals indicate that it is indeed similar to that of conceptual change in entire scientific fields. Individuals need to be confronted with anomalies that their preexisting theories cannot explain before entire conceptual structures are overthrown. However, replacement conceptual structures have to be generated before the old conceptual structure can be discarded. Sometimes, people do not overthrow their original conceptual theories and through their lives maintain their original views of many fundamental scientific concepts. Whether people actively possess naive theories, or whether they appear to have a naive theory because of the demand characteristics of the testing context, is a lively source of debate within the science education community (see Gupta, Hammer, & Redish, 2010 ).

Scientific Thinking in Children

Well before their first birthday, children appear to know several fundamental facts about the physical world. For example, studies with infants show that they behave as if they understand that solid objects endure over time (e.g., they don't just disappear and reappear, they cannot move through each other, and they move as a result of collisions with other solid objects or the force of gravity (Baillargeon, 2004 ; Carey 1985 ; Cohen & Cashon, 2006 ; Duschl, Schweingruber, & Shouse, 2007 ; Gelman & Baillargeon, 1983 ; Gelman & Kalish, 2006 ; Mandler, 2004 ; Metz 1995 ; Munakata, Casey, & Diamond, 2004 ). And even 6-month-olds are able to predict the future location of a moving object that they are attempting to grasp (Von Hofsten, 1980 ; Von Hofsten, Feng, & Spelke, 2000 ). In addition, they appear to be able to make nontrivial inferences about causes and their effects (Gopnik et al., 2004 ).

The similarities between children's thinking and scientists' thinking have an inherent allure and an internal contradiction. The allure resides in the enthusiastic wonder and openness with which both children and scientists approach the world around them. The paradox comes from the fact that different investigators of children's thinking have reached diametrically opposing conclusions about just how “scientific” children's thinking really is. Some claim support for the “child as a scientist” position (Brewer & Samarapungavan, 1991 ; Gelman & Wellman, 1991 ; Gopnik, Meltzoff, & Kuhl, 1999 ; Karmiloff-Smith 1988 ; Sodian, Zaitchik, & Carey, 1991 ; Samarapungavan 1992 ), while others offer serious challenges to the view (Fay & Klahr, 1996 ; Kern, Mirels, & Hinshaw, 1983 ; Kuhn, Amsel, & O'Laughlin, 1988 ; Schauble & Glaser, 1990 ; Siegler & Liebert, 1975 .) Such fundamentally incommensurate conclusions suggest that this very field—children's scientific thinking—is ripe for a conceptual revolution!

A recent comprehensive review (Duschl, Schweingruber, & Shouse, 2007 ) of what children bring to their science classes offers the following concise summary of the extensive developmental and educational research literature on children's scientific thinking:

Children entering school already have substantial knowledge of the natural world, much of which is implicit.

What children are capable of at a particular age is the result of a complex interplay among maturation, experience, and instruction. What is developmentally appropriate is not a simple function of age or grade, but rather is largely contingent on children's prior opportunities to learn.

Students' knowledge and experience play a critical role in their science learning, influencing four aspects of science understanding, including (a) knowing, using, and interpreting scientific explanations of the natural world; (b) generating and evaluating scientific evidence and explanations, (c) understanding how scientific knowledge is developed in the scientific community, and (d) participating in scientific practices and discourse.

Students learn science by actively engaging in the practices of science.

In the previous section of this article we discussed conceptual change with respect to scientific fields and undergraduate science students. However, the idea that children undergo radical conceptual change in which old “theories” need to be overthrown and reorganized has been a central topic in understanding changes in scientific thinking in both children and across the life span. This radical conceptual change is thought to be necessary for acquiring many new concepts in physics and is regarded as the major source of difficulty for students. The factors that are at the root of this conceptual shift view have been difficult to determine, although there have been a number of studies in cognitive development (Carey, 1985 ; Chi 1992 ; Chi & Roscoe, 2002 ), in the history of science (Thagard, 1992 ), and in physics education (Clement, 1982 ; Mestre 1991 ) that give detailed accounts of the changes in knowledge representation that occur while people switch from one way of representing scientific knowledge to another.

One area where students show great difficulty in understanding scientific concepts is physics. Analyses of students' changing conceptions, using interviews, verbal protocols, and behavioral outcome measures, indicate that large-scale changes in students' concepts occur in physics education (see McDermott & Redish, 1999 , for a review of this literature). Following Kuhn ( 1962 ), many researchers, but not all, have noted that students' changing conceptions resemble the sequences of conceptual changes in physics that have occurred in the history of science. These notions of radical paradigm shifts and ensuing incompatibility with past knowledge-states have called attention to interesting parallels between the development of particular scientific concepts in children and in the history of physics. Investigations of nonphysicists' understanding of motion indicate that students have extensive misunderstandings of motion. Some researchers have interpreted these findings as an indication that many people hold erroneous beliefs about motion similar to a medieval “impetus” theory (McCloskey, Caramazza, & Green, 1980 ). Furthermore, students appear to maintain “impetus” notions even after one or two courses in physics. In fact, some authors have noted that students who have taken one or two courses in physics can perform worse on physics problems than naive students (Mestre, 1991 ). Thus, it is only after extensive learning that we see a conceptual shift from impetus theories of motion to Newtonian scientific theories.

How one's conceptual representation shifts from “naive” to Newtonian is a matter of contention, as some have argued that the shift involves a radical conceptual change, whereas others have argued that the conceptual change is not really complete. For example, Kozhevnikov and Hegarty ( 2001 ) argue that much of the naive impetus notions of motion are maintained at the expense of Newtonian principles even with extensive training in physics. However, they argue that such impetus principles are maintained at an implicit level. Thus, although students can give the correct Newtonian answer to problems, their reaction times to respond indicate that they are also using impetus theories when they respond. An alternative view of conceptual change focuses on whether there are real conceptual changes at all. Gupta, Hammer and Redish ( 2010 ) and Disessa ( 2004 ) have conducted detailed investigations of changes in physics students' accounts of phenomena covered in elementary physics courses. They have found that rather than students possessing a naive theory that is replaced by the standard theory, many introductory physics students have no stable physical theory but rather construct their explanations from elementary pieces of knowledge of the physical world.

Computational Approaches to Scientific Thinking

Computational approaches have provided a more complete account of the scientific mind. Computational models provide specific detailed accounts of the cognitive processes underlying scientific thinking. Early computational work consisted of taking a scientific discovery and building computational models of the reasoning processes involved in the discovery. Langley, Simon, Bradshaw, and Zytkow ( 1987 ) built a series of programs that simulated discoveries such as those of Copernicus, Bacon, and Stahl. These programs had various inductive reasoning algorithms built into them, and when given the data that the scientists used, they were able to propose the same rules. Computational models make it possible to propose detailed models of the cognitive subcomponents of scientific thinking that specify exactly how scientific theories are generated, tested, and amended (see Darden, 1997 , and Shrager & Langley, 1990 , for accounts of this branch of research). More recently, the incorporation of scientific knowledge into computer programs has resulted in a shift in emphasis from using programs to simulate discoveries to building programs that are used to help scientists make discoveries. A number of these computer programs have made novel discoveries. For example, Valdes-Perez ( 1994 ) has built systems for discoveries in chemistry, and Fajtlowicz has done this in mathematics (Erdos, Fajtlowicz, & Staton, 1991 ).

These advances in the fields of computer discovery have led to new fields, conferences, journals, and even departments that specialize in the development of programs devised to search large databases in the hope of making new scientific discoveries (Langley, 2000 , 2002 ). This process is commonly known as “data mining.” This approach has only proved viable relatively recently, due to advances in computer technology. Biswal et al. ( 2010 ), Mitchell ( 2009 ), and Yang ( 2009 ) provide recent reviews of data mining in different scientific fields. Data mining is at the core of drug discovery, our understanding of the human genome, and our understanding of the universe for a number of reasons. First, vast databases concerning drug actions, biological processes, the genome, the proteome, and the universe itself now exist. Second, the development of high throughput data-mining algorithms makes it possible to search for new drug targets, novel biological mechanisms, and new astronomical phenomena in relatively short periods of time. Research programs that took decades, such as the development of penicillin, can now be done in days (Yang, 2009 ).

Another recent shift in the use of computers in scientific discovery has been to have both computers and people make discoveries together, rather than expecting that computers make an entire scientific discovery. Now instead of using computers to mimic the entire scientific discovery process as used by humans, computers can use powerful algorithms that search for patterns on large databases and provide the patterns to humans who can then use the output of these computers to make discoveries, ranging from the human genome to the structure of the universe. However, there are some robots such as ADAM, developed by King ( 2011 ), that can actually perform the entire scientific process, from the generation of hypotheses, to the conduct of experiments and the interpretation of results, with little human intervention. The ongoing development of scientific robots by some scientists (King et al., 2009 ) thus continues the tradition started by Herbert Simon in the 1960s. However, many of the controversies as to whether the robot is a “real scientist” or not continue to the present (Evans & Rzhetsky, 2010 , Gianfelici, 2010 ; Haufe, Elliott, Burian, & O' Malley, 2010 ; O'Malley 2011 ).

Scientific Thinking and Science Education

Accounts of the nature of science and research on scientific thinking have had profound effects on science education along many levels, particularly in recent years. Science education from the 1900s until the 1970s was primarily concerned with teaching students both the content of science (such as Newton's laws of motion) or the methods that scientists need to use in their research (such as using experimental and control groups). Beginning in the 1980s, a number of reports (e.g., American Association for the Advancement of Science, 1993; National Commission on Excellence in Education, 1983; Rutherford & Ahlgren, 1991 ) stressed the need for teaching scientific thinking skills rather than just methods and content. The addition of scientific thinking skills to the science curriculum from kindergarten through adulthood was a major shift in focus. Many of the particular scientific thinking skills that have been emphasized are skills covered in previous sections of this chapter, such as teaching deductive and inductive thinking strategies. However, rather than focusing on one particular skill, such as induction, researchers in education have focused on how the different components of scientific thinking are put together in science. Furthermore, science educators have focused upon situations where science is conducted collaboratively, rather than being the product of one person thinking alone. These changes in science education parallel changes in methodologies used to investigate science, such as analyzing the ways that scientists think and reason in their laboratories.

By looking at science as a complex multilayered and group activity, many researchers in science education have adopted a constructivist approach. This approach sees learning as an active rather than a passive process, and it suggests that students learn through constructing their scientific knowledge. We will first describe a few examples of the constructivist approach to science education. Following that, we will address several lines of work that challenge some of the assumptions of the constructivist approach to science education.

Often the goal of constructivist science education is to produce conceptual change through guided instruction where the teacher or professor acts as a guide to discovery, rather than the keeper of all the facts. One recent and influential approach to science education is the inquiry-based learning approach. Inquiry-based learning focuses on posing a problem or a puzzling event to students and asking them to propose a hypothesis that could explain the event. Next, the student is asked to collect data that test the hypothesis, make conclusions, and then reflect upon both the original problem and the thought processes that they used to solve the problem. Often students use computers that aid in their construction of new knowledge. The computers allow students to learn many of the different components of scientific thinking. For example, Reiser and his colleagues have developed a learning environment for biology, where students are encouraged to develop hypotheses in groups, codify the hypotheses, and search databases to test these hypotheses (Reiser et al., 2001 ).

One of the myths of science is the lone scientist suddenly shouting “Eureka, I have made a discovery!” Instead, in vivo studies of scientists (e.g., Dunbar, 1995 , 2002 ), historical analyses of scientific discoveries (Nersessian, 1999 ), and studies of children learning science at museums have all pointed to collaborative scientific discovery mechanisms as being one of the driving forces of science (Atkins et al., 2009 ; Azmitia & Crowley, 2001 ). What happens during collaborative scientific thinking is that there is usually a triggering event, such as an unexpected result or situation that a student does not understand. This results in other members of the group adding new information to the person's representation of knowledge, often adding new inductions and deductions that both challenge and transform the reasoner's old representations of knowledge (Chi & Roscoe, 2002 ; Dunbar 1998 ). Social mechanisms play a key component in fostering changes in concepts that have been ignored in traditional cognitive research but are crucial for both science and science education. In science education there has been a shift to collaborative learning, particularly at the elementary level; however, in university education, the emphasis is still on the individual scientist. As many domains of science now involve collaborations across scientific disciplines, we expect the explicit teaching of heuristics for collaborative science to increase.

What is the best way to teach and learn science? Surprisingly, the answer to this question has been difficult to uncover. For example, toward the end of the last century, influenced by several thinkers who advocated a constructivist approach to learning, ranging from Piaget (Beilin, 1994 ) to Papert ( 1980 ), many schools answered this question by adopting a philosophy dubbed “discovery learning.” Although a clear operational definition of this approach has yet to be articulated, the general idea is that children are expected to learn science by reconstructing the processes of scientific discovery—in a range of areas from computer programming to chemistry to mathematics. The premise is that letting students discover principles on their own, set their own goals, and collaboratively explore the natural world produces deeper knowledge that transfers widely.

The research literature on science education is far from consistent in its use of terminology. However, our reading suggests that “discovery learning” differs from “inquiry-based learning” in that few, if any, guidelines are given to students in discovery learning contexts, whereas in inquiry learning, students are given hypotheses and specific goals to achieve (see the second paragraph of this section for a definition of inquiry-based learning). Even though thousands of schools have adopted discovery learning as an alternative to more didactic approaches to teaching and learning, the evidence showing that it is more effective than traditional, direct, teacher-controlled instructional approaches is mixed, at best (Lorch et al., 2010 ; Minner, Levy, & Century, 2010 ). In several cases where the distinctions between direct instruction and more open-ended constructivist instruction have been clearly articulated, implemented, and assessed, direct instruction has proven to be superior to the alternatives (Chen & Klahr, 1999 ; Toth, Klahr, & Chen, 2000 ). For example, in a study of third- and fourth-grade children learning about experimental design, Klahr and Nigam ( 2004 ) found that many more children learned from direct instruction than from discovery learning. Furthermore, they found that among the few children who did manage to learn from a discovery method, there was no better performance on a far transfer test of scientific reasoning than that observed for the many children who learned from direct instruction.

The idea of children learning most of their science through a process of self-directed discovery has some romantic appeal, and it may accurately describe the personal experience of a handful of world-class scientists. However, the claim has generated some contentious disagreements (Kirschner, Sweller, & Clark, 2006 ; Klahr, 2010 ; Taber 2009 ; Tobias & Duffy, 2009 ), and the jury remains out on the extent to which most children can learn science that way.

Conclusions and Future Directions

The field of scientific thinking is now a thriving area of research with strong underpinnings in cognitive psychology and cognitive science. In recent years, a new professional society has been formed that aims to facilitate this integrative and interdisciplinary approach to the psychology of science, with its own journal and regular professional meetings. 1 Clearly the relations between these different aspects of scientific thinking need to be combined in order to produce a truly comprehensive picture of the scientific mind.

While much is known about certain aspects of scientific thinking, much more remains to be discovered. In particular, there has been little contact between cognitive, neuroscience, social, personality, and motivational accounts of scientific thinking. Research in thinking and reasoning has been expanded to use the methods and theories of cognitive neuroscience (see Morrison & Knowlton, Chapter 6 ). A similar approach can be taken in exploring scientific thinking (see Dunbar et al., 2007 ). There are two main reasons for taking a neuroscience approach to scientific thinking. First, functional neuroimaging allows the researcher to look at the entire human brain, making it possible to see the many different sites that are involved in scientific thinking and gain a more complete understanding of the entire range of mechanisms involved in this type of thought. Second, these brain-imaging approaches allow researchers to address fundamental questions in research on scientific thinking, such as the extent to which ordinary thinking in nonscientific contexts and scientific thinking recruit similar versus disparate neural structures of the brain.

Dunbar ( 2009 ) has used some novel methods to explore Simon's assertion, cited at the beginning of this chapter, that scientific thinking uses the same cognitive mechanisms that all human beings possess (rather than being an entirely different type of thinking) but combines them in ways that are specific to a particular aspect of science or a specific discipline of science. For example, Fugelsang and Dunbar ( 2009 ) compared causal reasoning when two colliding circular objects were labeled balls or labeled subatomic particles. They obtained different brain activation patterns depending on whether the stimuli were labeled balls or subatomic particles. In another series of experiments, Dunbar and colleagues used functional magnetic resonance imaging (fMRI) to study patterns of activation in the brains of students who have and who have not undergone conceptual change in physics. For example, Fugelsang and Dunbar ( 2005 ) and Dunbar et al. ( 2007 ) have found differences in the activation of specific brain sites (such as the anterior cingulate) for students when they encounter evidence that is inconsistent with their current conceptual understandings. These initial cognitive neuroscience investigations have the potential to reveal the ways that knowledge is organized in the scientific brain and provide detailed accounts of the nature of the representation of scientific knowledge. Petitto and Dunbar ( 2004 ) proposed the term “educational neuroscience” for the integration of research on education, including science education, with research on neuroscience. However, see Fitzpatrick (in press) for a very different perspective on whether neuroscience approaches are relevant to education. Clearly, research on the scientific brain is just beginning. We as scientists are beginning to get a reasonable grasp of the inner workings of the subcomponents of the scientific mind (i.e., problem solving, analogy, induction). However, great advances remain to be made concerning how these processes interact so that scientific discoveries can be made. Future research will focus on both the collaborative aspects of scientific thinking and the neural underpinnings of the scientific mind.

The International Society for the Psychology of Science and Technology (ISPST). Available at http://www.ispstonline.org/

Ahn, W., Kalish, C. W., Medin, D. L., & Gelman, S. A. ( 1995 ). The role of covariation versus mechanism information in causal attribution.   Cognition , 54 , 299–352.

American Association for the Advancement of Science. ( 1993 ). Benchmarks for scientific literacy . New York: Oxford University Press.

Google Scholar

Google Preview

Atkins, L. J., Velez, L., Goudy, D., & Dunbar, K. N. ( 2009 ). The unintended effects of interactive objects and labels in the science museum.   Science Education , 54 , 161–184.

Azmitia, M. A., & Crowley, K. ( 2001 ). The rhythms of scientific thinking: A study of collaboration in an earthquake microworld. In K. Crowley, C. Schunn, & T. Okada (Eds.), Designing for science: Implications from everyday, classroom, and professional settings (pp. 45–72). Mahwah, NJ: Erlbaum.

Bacon, F. ( 1620 /1854). Novum organum (B. Monatgue, Trans.). Philadelphia, P A: Parry & McMillan.

Baillargeon, R. ( 2004 ). Infants' reasoning about hidden objects: Evidence for event-general and event-specific expectations (article with peer commentaries and response, listed below).   Developmental Science , 54 , 391–424.

Baker, L. M., & Dunbar, K. ( 2000 ). Experimental design heuristics for scientific discovery: The use of baseline and known controls.   International Journal of Human Computer Studies , 54 , 335–349.

Beilin, H. ( 1994 ). Jean Piaget's enduring contribution to developmental psychology. In R. D. Parke, P. A. Ornstein, J. J. Rieser, & C. Zahn-Waxler (Eds.), A century of developmental psychology (pp. 257–290). Washington, DC US: American Psychological Association.

Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S.M., et al. ( 2010 ). Toward discovery science of human brain function.   Proceedings of the National Academy of Sciences of the United States of America , 107, 4734–4739.

Brewer, W. F., & Samarapungavan, A. ( 1991 ). Children's theories vs. scientific theories: Differences in reasoning or differences in knowledge? In R. R. Hoffman & D. S. Palermo (Eds.), Cognition and the symbolic processes: Applied and ecological perspectives (pp. 209–232). Hillsdale, NJ: Erlbaum.

Bruner, J. S., Goodnow, J. J., & Austin, G. A. ( 1956 ). A study of thinking . New York: NY Science Editions.

Carey, S. ( 1985 ). Conceptual change in childhood . Cambridge, MA: MIT Press.

Carruthers, P., Stich, S., & Siegal, M. ( 2002 ). The cognitive basis of science . New York: Cambridge University Press.

Chi, M. ( 1992 ). Conceptual change within and across ontological categories: Examples from learning and discovery in science. In R. Giere (Ed.), Cognitive models of science (pp. 129–186). Minneapolis: University of Minnesota Press.

Chi, M. T. H., & Roscoe, R. D. ( 2002 ). The processes and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp 3–27). Amsterdam, Netherlands: Kluwer Academic Publishers.

Chen, Z., & Klahr, D. ( 1999 ). All other things being equal: Children's acquisition of the control of variables strategy.   Child Development , 54 (5), 1098–1120.

Clement, J. ( 1982 ). Students' preconceptions in introductory mechanics.   American Journal of Physics , 54 , 66–71.

Cohen, L. B., & Cashon, C. H. ( 2006 ). Infant cognition. In W. Damon & R. M. Lerner (Series Eds.) & D. Kuhn & R. S. Siegler (Vol. Eds.), Handbook of child psychology. Vol. 2: Cognition, perception, and language (6th ed., pp. 214–251). New York: Wiley.

National Commission on Excellence in Education. ( 1983 ). A nation at risk: The imperative for educational reform . Washington, DC: US Department of Education.

Crick, F. H. C. ( 1988 ). What mad pursuit: A personal view of science . New York: Basic Books.

Darden, L. ( 2002 ). Strategies for discovering mechanisms: Schema instantiation, modular subassembly, forward chaining/backtracking.   Philosophy of Science , 69, S354–S365.

Davenport, J. L., Yaron, D., Klahr, D., & Koedinger, K. ( 2008 ). Development of conceptual understanding and problem solving expertise in chemistry. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 751–756). Austin, TX: Cognitive Science Society.

diSessa, A. A. ( 2004 ). Contextuality and coordination in conceptual change. In E. Redish & M. Vicentini (Eds.), Proceedings of the International School of Physics “Enrico Fermi:” Research on physics education (pp. 137–156). Amsterdam, Netherlands: ISO Press/Italian Physics Society

Dunbar, K. ( 1995 ). How scientists really reason: Scientific reasoning in real-world laboratories. In R. J. Sternberg, & J. Davidson (Eds.), Mechanisms of insight (pp. 365–395). Cambridge, MA: MIT press.

Dunbar, K. ( 1997 ). How scientists think: Online creativity and conceptual change in science. In T. B. Ward, S. M. Smith, & S. Vaid (Eds.), Conceptual structures and processes: Emergence, discovery and change (pp. 461–494). Washington, DC: American Psychological Association.

Dunbar, K. ( 1998 ). Problem solving. In W. Bechtel & G. Graham (Eds.), A companion to cognitive science (pp. 289–298). London: Blackwell

Dunbar, K. ( 1999 ). The scientist InVivo : How scientists think and reason in the laboratory. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 85–100). New York: Plenum.

Dunbar, K. ( 2001 ). The analogical paradox: Why analogy is so easy in naturalistic settings, yet so difficult in the psychology laboratory. In D. Gentner, K. J. Holyoak, & B. Kokinov Analogy: Perspectives from cognitive science (pp. 313–334). Cambridge, MA: MIT press.

Dunbar, K. ( 2002 ). Science as category: Implications of InVivo science for theories of cognitive development, scientific discovery, and the nature of science. In P. Caruthers, S. Stich, & M. Siegel (Eds.) Cognitive models of science (pp. 154–170). New York: Cambridge University Press.

Dunbar, K. ( 2009 ). The biology of physics: What the brain reveals about our physical understanding of the world. In M. Sabella, C. Henderson, & C. Singh. (Eds.), Proceedings of the Physics Education Research Conference (pp. 15–18). Melville, NY: American Institute of Physics.

Dunbar, K., & Fugelsang, J. ( 2004 ). Causal thinking in science: How scientists and students interpret the unexpected. In M. E. Gorman, A. Kincannon, D. Gooding, & R. D. Tweney (Eds.), New directions in scientific and technical thinking (pp. 57–59). Mahway, NJ: Erlbaum.

Dunbar, K., Fugelsang, J., & Stein, C. ( 2007 ). Do naïve theories ever go away? In M. Lovett & P. Shah (Eds.), Thinking with Data: 33 rd Carnegie Symposium on Cognition (pp. 193–206). Mahwah, NJ: Erlbaum.

Dunbar, K., & Sussman, D. ( 1995 ). Toward a cognitive account of frontal lobe function: Simulating frontal lobe deficits in normal subjects.   Annals of the New York Academy of Sciences , 54 , 289–304.

Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). ( 2007 ). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.

Einstein, A. ( 1950 ). Out of my later years . New York: Philosophical Library

Erdos, P., Fajtlowicz, S., & Staton, W. ( 1991 ). Degree sequences in the triangle-free graphs,   Discrete Mathematics , 54 (91), 85–88.

Evans, J., & Rzhetsky, A. ( 2010 ). Machine science.   Science , 54 , 399–400.

Fay, A., & Klahr, D. ( 1996 ). Knowing about guessing and guessing about knowing: Preschoolers' understanding of indeterminacy.   Child Development , 54 , 689–716.

Fischler, H., & Lichtfeldt, M. ( 1992 ). Modern physics and students conceptions.   International Journal of Science Education , 54 , 181–190.

Fitzpatrick, S. M. (in press). Functional brain imaging: Neuro-turn or wrong turn? In M. M., Littlefield & J.M., Johnson (Eds.), The neuroscientific turn: Transdisciplinarity in the age of the brain. Ann Arbor: University of Michigan Press.

Fox-Keller, E. ( 1985 ). Reflections on gender and science . New Haven, CT: Yale University Press.

Fugelsang, J., & Dunbar, K. ( 2005 ). Brain-based mechanisms underlying complex causal thinking.   Neuropsychologia , 54 , 1204–1213.

Fugelsang, J., & Dunbar, K. ( 2009 ). Brain-based mechanisms underlying causal reasoning. In E. Kraft (Ed.), Neural correlates of thinking (pp. 269–279). Berlin, Germany: Springer

Fugelsang, J., Stein, C., Green, A., & Dunbar, K. ( 2004 ). Theory and data interactions of the scientific mind: Evidence from the molecular and the cognitive laboratory.   Canadian Journal of Experimental Psychology , 54 , 132–141

Galilei, G. ( 1638 /1991). Dialogues concerning two new sciences (A. de Salvio & H. Crew, Trans.). Amherst, NY: Prometheus Books.

Galison, P. ( 2003 ). Einstein's clocks, Poincaré's maps: Empires of time . New York: W. W. Norton.

Gelman, R., & Baillargeon, R. ( 1983 ). A review of Piagetian concepts. In P. H. Mussen (Series Ed.) & J. H. Flavell & E. M. Markman (Vol. Eds.), Handbook of child psychology (4th ed., Vol. 3, pp. 167–230). New York: Wiley.

Gelman, S. A., & Kalish, C. W. ( 2006 ). Conceptual development. In D. Kuhn & R. Siegler (Eds.), Handbook of child psychology. Vol. 2: Cognition, perception and language (pp. 687–733). New York: Wiley.

Gelman, S., & Wellman, H. ( 1991 ). Insides and essences.   Cognition , 54 , 214–244.

Gentner, D. ( 2010 ). Bootstrapping the mind: Analogical processes and symbol systems.   Cognitive Science , 54 , 752–775.

Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P., & Forbus, K. D. ( 1997 ). Analogical reasoning and conceptual change: A case study of Johannes Kepler.   The Journal of the Learning Sciences , 54 (1), 3–40.

Gentner, D., Holyoak, K. J., & Kokinov, B. ( 2001 ). The analogical mind: Perspectives from cognitive science . Cambridge, MA: MIT Press.

Gentner, D., & Jeziorski, M. ( 1993 ). The shift from metaphor to analogy in western science. In A. Ortony (Ed.), Metaphor and thought (2nd ed., pp. 447–480). Cambridge, England: Cambridge University Press.

Gianfelici, F. ( 2010 ). Machine science: Truly machine-aided science.   Science , 54 , 317–319.

Giere, R. ( 1993 ). Cognitive models of science . Minneapolis: University of Minnesota Press.

Gopnik, A. N., Meltzoff, A. N., & Kuhl, P. K. ( 1999 ). The scientist in the crib: Minds, brains and how children learn . New York: Harper Collins

Gorman, M. E. ( 1989 ). Error, falsification and scientific inference: An experimental investigation.   Quarterly Journal of Experimental Psychology: Human Experimental Psychology , 41A , 385–412

Gorman, M. E., Kincannon, A., Gooding, D., & Tweney, R. D. ( 2004 ). New directions in scientific and technical thinking . Mahwah, NJ: Erlbaum.

Gupta, A., Hammer, D., & Redish, E. F. ( 2010 ). The case for dynamic models of learners' ontologies in physics.   Journal of the Learning Sciences , 54 (3), 285–321.

Haufe, C., Elliott, K. C., Burian, R., & O'Malley, M. A. ( 2010 ). Machine science: What's missing.   Science , 54 , 318–320.

Hecht, E. ( 2011 ). On defining mass.   The Physics Teacher , 54 , 40–43.

Heit, E. ( 2000 ). Properties of inductive reasoning.   Psychonomic Bulletin and Review , 54 , 569–592.

Holyoak, K. J., & Thagard, P. ( 1995 ). Mental leaps . Cambridge, MA: MIT Press.

Karmiloff-Smith, A. ( 1988 ) The child is a theoretician, not an inductivist.   Mind and Language , 54 , 183–195.

Keil, F. C. ( 1999 ). Conceptual change. In R. Wilson & F. Keil (Eds.), The MIT encyclopedia of cognitive science . (pp. 179–182) Cambridge, MA: MIT press.

Kern, L. H., Mirels, H. L., & Hinshaw, V. G. ( 1983 ). Scientists' understanding of propositional logic: An experimental investigation.   Social Studies of Science , 54 , 131–146.

King, R. D. ( 2011 ). Rise of the robo scientists.   Scientific American , 54 (1), 73–77.

King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., et al. ( 2009 ). The automation of science.   Science , 54 , 85–89.

Kirschner, P. A., Sweller, J., & Clark, R. ( 2006 ) Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching.   Educational Psychologist , 54 , 75–86

Klahr, D. ( 2000 ). Exploring science: The cognition and development of discovery processes . Cambridge, MA: MIT Press.

Klahr, D. ( 2010 ). Coming up for air: But is it oxygen or phlogiston? A response to Taber's review of constructivist instruction: Success or failure?   Education Review , 54 (13), 1–6.

Klahr, D., & Dunbar, K. ( 1988 ). Dual space search during scientific reasoning.   Cognitive Science , 54 , 1–48.

Klahr, D., & Nigam, M. ( 2004 ). The equivalence of learning paths in early science instruction: effects of direct instruction and discovery learning.   Psychological Science , 54 (10), 661–667.

Klahr, D. & Masnick, A. M. ( 2002 ). Explaining, but not discovering, abduction. Review of L. Magnani (2001) abduction, reason, and science: Processes of discovery and explanation.   Contemporary Psychology , 47, 740–741.

Klahr, D., & Simon, H. ( 1999 ). Studies of scientific discovery: Complementary approaches and convergent findings.   Psychological Bulletin , 54 , 524–543.

Klayman, J., & Ha, Y. ( 1987 ). Confirmation, disconfirmation, and information in hypothesis testing.   Psychological Review , 54 , 211–228.

Kozhevnikov, M., & Hegarty, M. ( 2001 ). Impetus beliefs as default heuristic: Dissociation between explicit and implicit knowledge about motion.   Psychonomic Bulletin and Review , 54 , 439–453.

Kuhn, T. ( 1962 ). The structure of scientific revolutions . Chicago, IL: University of Chicago Press.

Kuhn, D., Amsel, E., & O'Laughlin, M. ( 1988 ). The development of scientific thinking skills . Orlando, FL: Academic Press.

Kulkarni, D., & Simon, H. A. ( 1988 ). The processes of scientific discovery: The strategy of experimentation.   Cognitive Science , 54 , 139–176.

Langley, P. ( 2000 ). Computational support of scientific discovery.   International Journal of Human-Computer Studies , 54 , 393–410.

Langley, P. ( 2002 ). Lessons for the computational discovery of scientific knowledge. In Proceedings of the First International Workshop on Data Mining Lessons Learned (pp. 9–12).

Langley, P., Simon, H. A., Bradshaw, G. L., & Zytkow, J. M. ( 1987 ). Scientific discovery: Computational explorations of the creative processes . Cambridge, MA: MIT Press.

Lorch, R. F., Jr., Lorch, E. P., Calderhead, W. J., Dunlap, E. E., Hodell, E. C., & Freer, B. D. ( 2010 ). Learning the control of variables strategy in higher and lower achieving classrooms: Contributions of explicit instruction and experimentation.   Journal of Educational Psychology , 54 (1), 90–101.

Magnani, L., Carnielli, W., & Pizzi, C., (Eds.) ( 2010 ). Model-based reasoning in science and technology: Abduction, logic,and computational discovery. Series Studies in Computational Intelligence (Vol. 314). Heidelberg/Berlin: Springer.

Mandler, J.M. ( 2004 ). The foundations of mind: Origins of conceptual thought . Oxford, England: Oxford University Press.

Macpherson, R., & Stanovich, K. E. ( 2007 ). Cognitive ability, thinking dispositions, and instructional set as predictors of critical thinking.   Learning and Individual Differences , 54 , 115–127.

McCloskey, M., Caramazza, A., & Green, B. ( 1980 ). Curvilinear motion in the absence of external forces: Naive beliefs about the motion of objects.   Science , 54 , 1139–1141.

McDermott, L. C., & Redish, L. ( 1999 ). Research letter on physics education research.   American Journal of Psychics , 54 , 755.

Mestre, J. P. ( 1991 ). Learning and instruction in pre-college physical science.   Physics Today , 54 , 56–62.

Metz, K. E. ( 1995 ). Reassessment of developmental constraints on children's science instruction.   Review of Educational Research , 54 (2), 93–127.

Minner, D. D., Levy, A. J., & Century, J. ( 2010 ). Inquiry-based science instruction—what is it and does it matter? Results from a research synthesis years 1984 to 2002.   Journal of Research in Science Teaching , 54 (4), 474–496.

Mitchell, T. M. ( 2009 ). Mining our reality.   Science , 54 , 1644–1645.

Mitroff, I. ( 1974 ). The subjective side of science . Amsterdam, Netherlands: Elsevier.

Munakata, Y., Casey, B. J., & Diamond, A. ( 2004 ). Developmental cognitive neuroscience: Progress and potential.   Trends in Cognitive Sciences , 54 , 122–128.

Mynatt, C. R., Doherty, M. E., & Tweney, R. D. ( 1977 ) Confirmation bias in a simulated research environment: An experimental study of scientific inference.   Quarterly Journal of Experimental Psychology , 54 , 89–95.

Nersessian, N. ( 1998 ). Conceptual change. In W. Bechtel, & G. Graham (Eds.), A companion to cognitive science (pp. 157–166). London, England: Blackwell.

Nersessian, N. ( 1999 ). Models, mental models, and representations: Model-based reasoning in conceptual change. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 5–22). New York: Plenum.

Nersessian, N. J. ( 2002 ). The cognitive basis of model-based reasoning in science In. P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 133–152). New York: Cambridge University Press.

Nersessian, N. J. ( 2008 ) Creating scientific concepts . Cambridge, MA: MIT Press.

O' Malley, M. A. ( 2011 ). Exploration, iterativity and kludging in synthetic biology.   Comptes Rendus Chimie , 54 (4), 406–412 .

Papert, S. ( 1980 ) Mindstorms: Children computers and powerful ideas. New York: Basic Books.

Penner, D. E., & Klahr, D. ( 1996 ). When to trust the data: Further investigations of system error in a scientific reasoning task.   Memory and Cognition , 54 (5), 655–668.

Petitto, L. A., & Dunbar, K. ( 2004 ). New findings from educational neuroscience on bilingual brains, scientific brains, and the educated mind. In K. Fischer & T. Katzir (Eds.), Building usable knowledge in mind, brain, and education Cambridge, England: Cambridge University Press.

Popper, K. R. ( 1959 ). The logic of scientific discovery . London, England: Hutchinson.

Qin, Y., & Simon, H.A. ( 1990 ). Laboratory replication of scientific discovery processes.   Cognitive Science , 54 , 281–312.

Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., & Leone, T. J., ( 2001 ). BGuILE: Stategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263–306). Mahwah, NJ: Erlbaum

Riordan, M., Rowson, P. C., & Wu, S. L. ( 2001 ). The search for the higgs boson.   Science , 54 , 259–260.

Rutherford, F. J., & Ahlgren, A. ( 1991 ). Science for all Americans. New York: Oxford University Press.

Samarapungavan, A. ( 1992 ). Children's judgments in theory choice tasks: Scientifc rationality in childhood.   Cognition , 54 , 1–32.

Schauble, L., & Glaser, R. ( 1990 ). Scientific thinking in children and adults. In D. Kuhn (Ed.), Developmental perspectives on teaching and learning thinking skills. Contributions to Human Development , (Vol. 21, pp. 9–26). Basel, Switzerland: Karger.

Schunn, C. D., & Klahr, D. ( 1995 ). A 4-space model of scientific discovery. In Proceedings of the 17th Annual Conference of the Cognitive Science Society (pp. 106–111). Mahwah, NJ: Erlbaum.

Schunn, C. D., & Klahr, D. ( 1996 ). The problem of problem spaces: When and how to go beyond a 2-space model of scientific discovery. Part of symposium on Building a theory of problem solving and scientific discovery: How big is N in N-space search? In Proceedings of the 18th Annual Conference of the Cognitive Science Society (pp. 25–26). Mahwah, NJ: Erlbaum.

Shrager, J., & Langley, P. ( 1990 ). Computational models of scientific discovery and theory formation . San Mateo, CA: Morgan Kaufmann.

Siegler, R. S., & Liebert, R. M. ( 1975 ). Acquisition of formal scientific reasoning by 10- and 13-year-olds: Designing a factorial experiment.   Developmental Psychology , 54 , 401–412.

Simon, H. A. ( 1977 ). Models of discovery . Dordrecht, Netherlands: D. Reidel Publishing.

Simon, H. A., Langley, P., & Bradshaw, G. L. ( 1981 ). Scientific discovery as problem solving.   Synthese , 54 , 1–27.

Simon, H. A., & Lea, G. ( 1974 ). Problem solving and rule induction. In H. Simon (Ed.), Models of thought (pp. 329–346). New Haven, CT: Yale University Press.

Smith, E. E., Shafir, E., & Osherson, D. ( 1993 ). Similarity, plausibility, and judgments of probability.   Cognition. Special Issue: Reasoning and decision making , 54 , 67–96.

Sodian, B., Zaitchik, D., & Carey, S. ( 1991 ). Young children's differentiation of hypothetical beliefs from evidence.   Child Development , 54 , 753–766.

Taber, K. S. ( 2009 ). Constructivism and the crisis in U.S. science education: An essay review.   Education Review , 54 (12), 1–26.

Thagard, P. ( 1992 ). Conceptual revolutions . Cambridge, MA: MIT Press.

Thagard, P. ( 1999 ). How scientists explain disease . Princeton, NJ: Princeton University Press.

Thagard, P., & Croft, D. ( 1999 ). Scientific discovery and technological innovation: Ulcers, dinosaur extinction, and the programming language Java. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 125–138). New York: Plenum.

Tobias, S., & Duffy, T. M. (Eds.). ( 2009 ). Constructivist instruction: Success or failure? New York: Routledge.

Toth, E. E., Klahr, D., & Chen, Z. ( 2000 ) Bridging research and practice: A cognitively-based classroom intervention for teaching experimentation skills to elementary school children.   Cognition and Instruction , 54 (4), 423–459.

Tweney, R. D. ( 1989 ). A framework for the cognitive psychology of science. In B. Gholson, A. Houts, R. A. Neimeyer, & W. Shadish (Eds.), Psychology of science: Contributions to metascience (pp. 342–366). Cambridge, England: Cambridge University Press.

Tweney, R. D., Doherty, M. E., & Mynatt, C. R. ( 1981 ). On scientific thinking . New York: Columbia University Press.

Valdes-Perez, R. E. ( 1994 ). Conjecturing hidden entities via simplicity and conservation laws: Machine discovery in chemistry.   Artificial Intelligence , 54 (2), 247–280.

Von Hofsten, C. ( 1980 ). Predictive reaching for moving objects by human infants.   Journal of Experimental Child Psychology , 54 , 369–382.

Von Hofsten, C., Feng, Q., & Spelke, E. S. ( 2000 ). Object representation and predictive action in infancy.   Developmental Science , 54 , 193–205.

Vosnaidou, S. (Ed.). ( 2008 ). International handbook of research on conceptual change . New York: Taylor & Francis.

Vosniadou, S., & Brewer, W. F. ( 1992 ). Mental models of the earth: A study of conceptual change in childhood.   Cognitive Psychology , 54 , 535–585.

Wason, P. C. ( 1968 ). Reasoning about a rule.   Quarterly Journal of Experimental Psychology , 54 , 273–281.

Wertheimer, M. ( 1945 ). Productive thinking . New York: Harper.

Yang, Y. ( 2009 ). Target discovery from data mining approaches.   Drug Discovery Today , 54 (3–4), 147–154.

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• Research Matters — to the Science Teacher

The Science Process Skills

Introduction.

One of the most important and pervasive goals of schooling is to teach students to think. All school subjects should share in accomplishing this overall goal. Science contributes its unique skills, with its emphasis on hypothesizing, manipulating the physical world and reasoning from data.

The scientific method, scientific thinking and critical thinking have been terms used at various times to describe these science skills. Today the term "science process skills" is commonly used. Popularized by the curriculum project, Science - A Process Approach (SAPA), these skills are defined as a set of broadly transferable abilities, appropriate to many science disciplines and reflective of the behavior of scientists. SAPA grouped process skills into two types-basic and integrated. The basic (simpler) process skills provide a foundation for learning the integrated (more complex) skills. These skills are listed and described below.

Basic Science Process Skills

Observing - using the senses to gather information about an object or event. Example: Describing a pencil as yellow. Inferring - making an "educated guess" about an object or event based on previously gathered data or information. Example: Saying that the person who used a pencil made a lot of mistakes because the eraser was well worn. Measuring - using both standard and nonstandard measures or estimates to describe the dimensions of an object or event. Example: Using a meter stick to measure the length of a table in centimeters. Communicating - using words or graphic symbols to describe an action, object or event. Example: Describing the change in height of a plant over time in writing or through a graph. Classifying - grouping or ordering objects or events into categories based on properties or criteria. Example: Placing all rocks having certain grain size or hardness into one group. Predicting - stating the outcome of a future event based on a pattern of evidence. Example: Predicting the height of a plant in two weeks time based on a graph of its growth during the previous four weeks.

Integrated Science Process Skills

Controlling variables - being able to identify variables that can affect an experimental outcome, keeping most constant while manipulating only the independent variable. Example: Realizing through past experiences that amount of light and water need to be controlled when testing to see how the addition of organic matter affects the growth of beans. Defining operationally - stating how to measure a variable in an experiment. Example: Stating that bean growth will be measured in centimeters per week. Formulating hypotheses - stating the expected outcome of an experiment. Example: The greater the amount of organic matter added to the soil, the greater the bean growth. Interpreting data - organizing data and drawing conclusions from it. Example: Recording data from the experiment on bean growth in a data table and forming a conclusion which relates trends in the data to variables. Experimenting - being able to conduct an experiment, including asking an appropriate question, stating a hypothesis, identifying and controlling variables, operationally defining those variables, designing a "fair" experiment, conducting the experiment, and interpreting the results of the experiment. Example: The entire process of conducting the experiment on the affect of organic matter on the growth of bean plants. Formulating models - creating a mental or physical model of a process or event. Examples: The model of how the processes of evaporation and condensation interrelate in the water cycle.

Learning basic process skills

Numerous research projects have focused on the teaching and acquisition of basic process skills. For example, Padilla, Cronin, and Twiest (1985) surveyed the basic process skills of 700 middle school students with no special process skill training. They found that only 10% of the students scored above 90% correct, even at the eighth grade level. Several researchers have found that teaching increases levels of skill performance. Thiel and George (1976) investigated predicting among third and fifth graders, and Tomera (1974) observing among seventh graders. From these studies it can be concluded that basic skills can be taught and that when learned, readily transferred to new situations (Tomera, 1974). Teaching strategies which proved effective were: (1) applying a set of specific clues for predicting, (2) using activities and pencil and paper simulations to teach graphing, and (3) using a combination of explaining, practice with objects, discussions and feedback with observing. In other words-just what research and theory has always defined as good teaching.

Other studies evaluated the effect of NSF-funded science curricula on how well they taught basic process skills. Studies focusing on the Science Curriculum Improvement Study (SCIS) and SAPA indicate that elementary school students, if taught process skills abilities, not only learn to use those processes, but also retain them for future use. Researchers, after comparing SAPA students to those experiencing a more traditional science program, concluded that the success of SAPA lies in the area of improving process oriented skills (Wideen, 1975; McGlathery, 1970). Thus it seems reasonable to conclude that students learn the basic skills better if they are considered an important object of instruction and if proven teaching methods are used.

Learning integrated process skills

Several studies have investigated the learning of integrated science process skills. Allen (1973) found that third graders can identify variables if the context is simple enough. Both Quinn and George (1975) and Wright (1981) found that students can be taught to formulate hypotheses and that this ability is retained over time.

Others have tried to teach all of the skills involved in conducting an experiment. Padilla, Okey and Garrard (1984) systematically integrated experimenting lessons into a middle school science curriculum. One group of students was taught a two week introductory unit on experimenting which focused on manipulative activities. A second group was taught the experimenting unit, but also experienced one additional process skill activity per week for a period of fourteen weeks. Those having the extended treatment outscored those experiencing the two week unit. These results indicate that the more complex process skills cannot be learned via a two week unit in which science content is typically taught. Rather, experimenting abilities need to be practiced over a period of time.

Further study of experimenting abilities shows that they are closely related to the formal thinking abilities described by Piaget. A correlation of +.73 between the two sets of abilities was found in one study (Padilla, Okey and Dillashaw, 1983). In fact, one of the ways that Piaget decided whether someone was formal or concrete was to ask that person to design an experiment to solve a problem. We also know that most early adolescents and many young adults have not yet reached their full formal reasoning capacity (Chiapetta, 1976). One study found only 17% of seventh graders and 34% of twelfth graders fully formal (Renner, Grant, and Sutherland, 1978).

What have we learned about teaching integrated science processes? We cannot expect students to excel at skills they have not experienced or been allowed to practice. Teachers cannot expect mastery of experimenting skills after only a few practice sessions. Instead students need multiple opportunities to work with these skills in different content areas and contexts. Teachers need to be patient with those having difficulties, since there is a need to have developed formal thinking patterns to successfully "experiment."

Summary and Conclusions

A reasonable portion of the science curriculum should emphasize science process skills according to the National Science Teachers Association. In general, the research literature indicates that when science process skills are a specific planned outcome of a science program, those skills can be learned by students. This was true with the SAPA and SCIS and other process skill studies cited in this review as well as with many other studies not cited.

Teachers need to select curricula which emphasize science process skills. In addition they need to capitalize on opportunities in the activities normally done in the classroom. While not an easy solution to implement, it remains the best available at this time because of the lack of emphasis of process skills in most commercial materials.

by Michael J. Padilla, Professor of Science Education, University of Georgia, Athens, GA

Allen, L. (1973). An examination of the ability of third grade children from the Science Curriculum Improvement Study to identify experimental variables and to recognize change.  Science Education, 57 , 123-151. Chiapetta, E. (1976). A review of Piagetian studies relevant to science instruction at the secondary and college level.  Science Education, 60 , 253-261. McGlathery, G. (1970). An assessment of science achievement of five and six-year-old students of contrasting socio-economic background.  Research and Curriculum Development in Science Education, 7023 , 76-83. McKenzie, D., & Padilla, M. (1984). Effect of laboratory activities and written simulations on the acquisition of graphing skills by eighth grade students. Paper presented at the annual meeting of the National Association for Research in Science Teaching, New Orleans. Padilla, M., Okey, J., & Dillashaw, F. (1983). The relationship between science process skills and formal thinking abilities.  Journal of Research in Science Teaching, 20 . Padilla, M., Cronin, L., & Twiest, M. (1985). The development and validation of the test of basic process skills. Paper presented at the annual meeting of the National Association for Research in Science Teaching, French Lick, IN. Quinn, M., & George, K. D. (1975). Teaching hypothesis formation.  Science Education, 59 , 289-296. Science Education, 62 , 215-221. Thiel, R., & George, D. K. (1976). Some factors affecting the use of the science process skill of prediction by elementary school children.  Journal of Research in Science Teaching, 13 , 155-166. Tomera, A. (1974). Transfer and retention of transfer of the science processes of observation and comparison in junior high school students.  Science Education, 58 , 195-203. Wideen, M. (1975). Comparison of student outcomes for Science - A Process Approach and traditional science teaching for third, fourth, fifth, and sixth grade classes: A product evaluation.  Journal of Research in Science Teaching, 12 , 31-39. Wright, E. (1981). The long-term effects of intensive instruction on the open exploration behavior of ninth grade students.  Journal of Research in Science Teaching, 18.