3 central components of scientific and critical thinking

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The Fundamentals of Scientific Thinking and Critical Analysis: A Comprehensive Guide

Scientific thinking and critical analysis are fundamental skills that play a crucial role in our daily lives. These skills help individuals to analyze information, evaluate arguments, and make informed decisions based on facts and evidence. The ability to think critically is especially important in the field of science, where scientists rely on logical reasoning and empirical evidence to understand the natural world.

Scientific thinking involves a systematic approach to problem-solving, where individuals use empirical evidence, logical reasoning, and critical thinking to develop hypotheses, test them, and draw conclusions. Critical analysis, on the other hand, involves evaluating information, arguments, and claims in a systematic and objective way to determine their validity and reliability. By combining these two skills, individuals can develop a deeper understanding of the world around them and make informed decisions based on evidence.

In today’s world, where information is readily available, the ability to think critically and analyze information is more important than ever. With so much information at our fingertips, it can be difficult to separate fact from fiction. The ability to think critically and evaluate sources of information is crucial to making informed decisions and avoiding misinformation. Therefore, understanding the fundamentals of scientific thinking and critical analysis is essential for anyone seeking to navigate the complex world of information and science.

Understanding Scientific Thinking

Scientific thinking is the thought process and reasoning involved in the field of science. It encompasses various techniques such as observation, induction, deduction, and experimental design. This section will provide an overview of the scientific method, experimental design, and systematic reasoning.

The Scientific Method

The scientific method is a systematic approach to investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. It involves the following steps:

• Define the purpose of the experiment
• Formulate a hypothesis
• Study the phenomenon and collect data
• Analyze the data
• Draw conclusions
• Communicate the results

Experimental Design

Experimental design involves the planning and execution of experiments to test hypotheses. It involves the following elements:

• Hypotheses: A hypothesis is a tentative explanation for an observation or phenomenon.
• Experiment: An experiment is a test of a hypothesis.
• Control: A control is an experimental condition that remains constant throughout the experiment.
• Variable: A variable is any factor that can change in an experiment.
• Control group: A control group is a group that is not exposed to the experimental treatment.
• Variables: Variables are factors that can change in an experiment.
• Data collection: Data collection involves the collection of data through observation or experimentation.
• Hypothesis testing: Hypothesis testing involves the use of statistical analysis to determine the probability that an observed effect is due to chance.

Systematic Reasoning

Systematic reasoning involves the use of logical and critical thinking to evaluate hypotheses and alternative explanations. It involves the following elements:

• Induction: Induction involves the use of observations to develop general principles or theories.
• Deduction: Deduction involves the use of general principles or theories to make predictions about specific observations.
• Alternative explanations: Alternative explanations are explanations that are different from the original hypothesis.
• Qualitative: Qualitative data is descriptive data that cannot be measured.
• Falsifiable: Falsifiable means that a hypothesis can be tested and potentially proven false.

Scientific thinking is fundamental to the fields of chemistry, physics, biology, and the study of the universe. It involves the use of controls to ensure the validity of experiments and the collection of data to support or refute hypotheses.

The Art of Critical Analysis

Critical analysis is an essential component of scientific thinking. It is a process of evaluating information, ideas, and arguments to form a well-reasoned judgment. The art of critical analysis involves the ability to identify and evaluate arguments, examine evidence, and detect bias. This section will explore the basics of critical analysis, including hypothesis and argument formation, and evaluating evidence.

Hypothesis and Argument Formation

Hypothesis and argument formation are crucial steps in critical analysis. A hypothesis is a proposed explanation for a phenomenon that can be tested through experimentation or observation. It is essential to form a hypothesis that is testable, falsifiable, and based on available evidence. An argument is a set of propositions that support or oppose a particular position. Arguments can be deductive or inductive and may involve premises, evidence, and conclusions.

When forming a hypothesis or argument, it is essential to consider the available evidence and avoid personal bias. Personal biases can influence hypothesis and argument formation, leading to confirmation bias, where individuals seek evidence that supports their pre-existing beliefs, and ignore evidence that contradicts it. It is essential to approach hypothesis and argument formation with an open mind and evaluate evidence objectively.

Evaluating Evidence

Evaluating evidence is a crucial step in critical analysis. Evidence can come in many forms, including data, expert opinions, and personal experiences. When evaluating evidence, it is essential to consider the reliability and objectivity of the source. Reliable evidence is based on accurate and verifiable data, while objective evidence is free from personal bias.

In addition to evaluating the reliability and objectivity of evidence, it is essential to examine the reasoning and logic behind the evidence. Sound reasoning involves using valid arguments that are based on premises that are true and relevant to the conclusion. It is essential to examine the reasoning behind the evidence and ensure that it is logical and valid.

In conclusion, critical analysis is an essential component of scientific thinking. It involves the ability to identify and evaluate arguments, examine evidence, and detect bias. Hypothesis and argument formation and evaluating evidence are crucial steps in critical analysis. When forming a hypothesis or argument and evaluating evidence, it is essential to consider personal biases, reliability, objectivity, reasoning, and logic. By approaching critical analysis with an open mind and evaluating evidence objectively, individuals can form well-reasoned judgments and make informed decisions.

Scientific Investigation and Research

Scientific investigation and research are essential components of scientific thinking and critical analysis. Research is a systematic process of collecting and analyzing data to answer a research question or test a hypothesis. It involves the use of various research methods to gather data, analyze it, and interpret the results.

Research Methods

Research methods are the techniques used to collect data. They can be qualitative or quantitative. Qualitative research methods are used to gather data that cannot be quantified, such as opinions and attitudes. Quantitative research methods are used to gather data that can be measured and analyzed statistically, such as numerical data.

Some common research methods used in scientific investigation include surveys, experiments, case studies, and observational studies. Each method has its strengths and weaknesses, and the choice of method depends on the research question and the type of data to be collected.

Data Analysis

Once the data has been collected, it is analyzed using statistical methods to identify trends and patterns. Data analysis involves the use of various statistical techniques to test the research hypothesis and draw conclusions from the data.

Interpreting Results

Interpreting research findings involves examining the data and drawing conclusions based on the results of the data analysis. It is important to interpret the results accurately and objectively to ensure the accuracy and validity of the research findings.

Variables are factors that can influence the outcome of the research. The independent variable is the factor that is manipulated in the study, while the dependent variable is the outcome that is measured. The sample size is the number of participants in the study.

Intervention is the process of manipulating the independent variable to observe its effect on the dependent variable. The research process involves selecting a research question, developing a hypothesis, selecting a research method, collecting data, analyzing the data, and interpreting the results.

Scientific investigation and research require a high degree of accuracy and attention to detail. It is important to ensure that the research is conducted ethically and that the results are reported accurately and objectively. By using appropriate research methods, analyzing the data, and interpreting the results accurately, researchers can make valuable contributions to the field of science.

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Bias and Objectivity in Scientific Thinking

Scientific thinking requires a commitment to objectivity, which is the idea that scientific questions, methods, and results should not be affected by personal biases or opinions. However, it is important to recognize that all scientists have some level of personal bias, which can influence their work.

Understanding and Identifying Bias

Bias can take many forms, including confirmation bias, which is the tendency to seek out information that confirms pre-existing beliefs and ignore information that contradicts them. Other biases include selection bias, which occurs when participants in a study are not representative of the population being studied, and publication bias, which occurs when studies with negative results are less likely to be published.

To identify bias in scientific research, it is important to look for potential sources of bias in the study design and analysis. For example, if a study is funded by a company that sells a product related to the study, there may be a conflict of interest that could bias the results. Similarly, if the study design is flawed or the sample size is too small, the results may not be reliable.

Maintaining Objectivity

To maintain objectivity in scientific thinking, it is important to be aware of personal biases and take steps to minimize their influence. This can include using standardized procedures and protocols to ensure that data collection and analysis are consistent and unbiased. It can also involve seeking out diverse perspectives and opinions to avoid groupthink and confirmation bias.

Maintaining objectivity also requires a commitment to transparency and openness in scientific research. This means openly sharing data and methods with other researchers and being willing to revise or retract findings if new evidence emerges.

In conclusion, while it is impossible to eliminate personal bias entirely, scientists can take steps to minimize its influence and maintain objectivity in their work. By being aware of potential sources of bias and taking steps to address them, scientists can ensure that their research is reliable and trustworthy.

The Role of Critical Thinking Skills

Critical thinking skills are essential for scientific thinking and critical analysis. They involve the ability to observe, interpret, question, reason, and make informed decisions based on acquired knowledge. Critical thinking skills enable individuals to analyze and evaluate information, ideas, and arguments to make informed decisions.

Observation and Interpretation

Observation is the first step in critical thinking. It involves the ability to gather information through the senses and interpret it objectively. Observation requires individuals to pay attention to details, identify patterns, and make connections between different pieces of information. Interpretation involves making sense of the information gathered through observation. It requires individuals to analyze and evaluate data to draw conclusions and make informed decisions.

Questioning and Reasoning

Questioning is an essential aspect of critical thinking. It involves the ability to ask relevant questions to clarify and evaluate information. Questioning enables individuals to identify assumptions, biases, and inconsistencies in arguments and ideas. Reasoning involves the ability to use logic and evidence to evaluate arguments and ideas critically. It requires individuals to identify and evaluate the strength and weaknesses of different arguments and ideas.

Making Informed Decisions

Making informed decisions is the ultimate goal of critical thinking. It involves the ability to use critical thinking skills to evaluate and analyze information to make informed decisions. Making informed decisions requires individuals to consider multiple perspectives, evaluate evidence, and weigh the pros and cons of different options. It also involves the ability to communicate ideas and arguments effectively and persuasively.

In conclusion, critical thinking skills are essential for scientific thinking and critical analysis. They involve the ability to observe, interpret, question, reason, and make informed decisions based on acquired knowledge. Critical thinking skills enable individuals to analyze and evaluate information, ideas, and arguments to make informed decisions.

Applying Scientific Thinking and Critical Analysis

Scientific thinking and critical analysis are essential skills that can be applied in various aspects of life, including everyday situations, academia, and research. By using these skills, individuals can evaluate information and make informed decisions based on evidence rather than opinions or assumptions.

In Everyday Life

In everyday life, scientific thinking and critical analysis can help individuals make informed decisions about their health, finances, and environment. For example, when evaluating health information, individuals can use scientific thinking to assess the credibility of sources and critically analyze the evidence presented. This can help them make informed decisions about their health and well-being.

Similarly, when making financial decisions, individuals can use critical analysis to evaluate investment opportunities and assess the potential risks and benefits. By applying scientific thinking and critical analysis, individuals can make informed decisions that are based on evidence rather than speculation or hearsay.

In Academia

In college and other academic settings, scientific thinking and critical analysis are essential skills that students need to develop to succeed. By applying these skills, students can evaluate information, analyze data, and make informed decisions about their academic work.

For example, when conducting research, students can use scientific thinking to develop hypotheses, design experiments, and analyze data. By using critical analysis, they can evaluate the credibility of sources and assess the quality of evidence presented.

In Research

In research, scientific thinking and critical analysis are essential skills that researchers need to develop to conduct rigorous and reliable studies. By applying these skills, researchers can design studies that are based on sound scientific principles and analyze data in a rigorous and systematic manner.

For example, when designing a study, researchers can use scientific thinking to develop hypotheses, design experiments, and select appropriate measures. By using critical analysis, they can evaluate the quality of evidence presented and assess the validity of their findings.

Overall, scientific thinking and critical analysis are essential skills that can be applied in various aspects of life. By developing these skills, individuals can evaluate information, analyze data, and make informed decisions based on evidence rather than opinions or assumptions.

The Influence of External Factors

Scientific thinking and critical analysis are not only influenced by internal factors such as cognitive skills, but also by external factors. These external factors can include the role of the author and expert, the impact of time and environment, and the effect of personal motivation. Understanding how these external factors can influence scientific thinking is crucial for researchers and students alike.

The Role of the Author and Expert

The author and expert play an important role in shaping scientific thinking. The credibility and reputation of the author or expert can influence how their work is perceived and accepted in the scientific community. For example, research conducted by top scholars in a field is often considered more credible and influential than research conducted by lesser-known scholars. In a study analyzing the relation between internal and external influences of top economics scholars, the number of pages indexed by Google and Bing was used as a measure of external influence. The study found that although the correlation between internal and external influence is low overall, it is highest among recipients of major key awards such as Nobel laureates.

The Impact of Time and Environment

Time and environment can also have a significant impact on scientific thinking. The cultural and social context in which research is conducted can influence the questions asked, the methods used, and the interpretations made. For example, research conducted in a certain time period may be influenced by the prevailing social and political attitudes of that time. Similarly, research conducted in different geographical regions may be influenced by the cultural norms and values of those regions.

The Effect of Personal Motivation

Personal motivation is another external factor that can influence scientific thinking. Researchers who are motivated by personal interests or financial gain may be more likely to pursue research that supports their interests or financial goals, rather than research that is objective and unbiased. In a study analyzing the factors related to critical thinking abilities of high school students, the significant internal factors were found to be intention and orientation in choosing the study program, while the significant external factors were found to be quality of education and the teacher’s ability to provide guidance.

In conclusion, external factors can have a significant impact on scientific thinking and critical analysis. Researchers and students should be aware of these external factors and take steps to mitigate their influence when conducting research or evaluating scientific claims. By doing so, they can ensure that their work is objective, unbiased, and credible.

Challenges and Misconceptions

Scientific thinking and critical analysis are not easy skills to master. There are many challenges and misconceptions that can hinder one’s ability to think critically. In this section, we will discuss some of the common misconceptions and challenges that people face when trying to think scientifically.

Common Misconceptions

One of the most common misconceptions about scientific thinking is that it is all about memorizing facts and figures. However, this is far from the truth. Scientific thinking is all about questioning assumptions, analyzing evidence, and making logical conclusions based on that evidence. It is not about blindly accepting what someone else tells you.

Another misconception is that scientific thinking is only for scientists. In reality, anyone can benefit from learning to think scientifically. Whether you are a student, a business person, or just someone who wants to make better decisions, scientific thinking can help you achieve your goals.

Overcoming Challenges

One of the biggest challenges with scientific thinking is overcoming our own biases and preconceptions. We all have our own beliefs and assumptions about the world, and these can sometimes get in the way of our ability to think critically. To overcome this challenge, it is important to be aware of our own biases and to actively work to overcome them.

Another challenge is dealing with misinformation and fake news. In today’s world, it is all too easy to be misled by false information. To overcome this challenge, it is important to be skeptical of information that seems too good to be true and to always verify the source of the information before accepting it as true.

In conclusion, scientific thinking and critical analysis are important skills that can help us make better decisions and lead more fulfilling lives. However, there are many challenges and misconceptions that can make it difficult to think scientifically. By being aware of these challenges and actively working to overcome them, we can all become better critical thinkers.

In conclusion, scientific thinking and critical analysis are essential skills for any individual who wants to make informed decisions and solve problems based on accurate and reliable information. The process of scientific thinking involves the application of logic, research, and methods to analyze data and draw conclusions based on evidence. It requires individuals to be unbiased, open-minded, and willing to challenge their assumptions and beliefs.

To develop these skills, individuals must have a strong foundation of knowledge on the subject matter they are analyzing. They must be able to identify and evaluate sources of information based on their accuracy and reliability. They must also be able to recognize and address biases that may affect their analysis and conclusions.

Accuracy is crucial in scientific thinking and critical analysis. Individuals must be able to distinguish between facts and opinions and use evidence-based reasoning to draw conclusions. They must also be able to communicate their findings clearly and concisely to others.

The purpose of scientific thinking and critical analysis is to improve our understanding of the world around us and to make informed decisions based on evidence. By applying these skills, individuals can solve complex problems, identify new opportunities, and contribute to the advancement of knowledge in their respective fields.

Overall, the importance of scientific thinking and critical analysis cannot be overstated. It is a fundamental aspect of human knowledge and progress, and its application has led to numerous breakthroughs and discoveries throughout history. As such, individuals who develop these skills are better equipped to navigate the complexities of the modern world and make informed decisions that positively impact their lives and those around them.

Frequently Asked Questions

What is the importance of scientific thinking in research.

Scientific thinking is crucial in research as it helps to ensure that the research is conducted in a systematic and objective manner. By using scientific thinking, researchers are able to develop hypotheses, design experiments, and analyze data in a way that minimizes bias and maximizes the reliability of the results. Scientific thinking is therefore essential for producing accurate and trustworthy research findings.

What are some examples of scientific thinking in everyday life?

Scientific thinking is not limited to research settings and can be applied in everyday life as well. Examples of scientific thinking in everyday life include using evidence to support arguments, evaluating claims based on data and facts, and making decisions based on logical reasoning. Scientific thinking can also involve questioning assumptions, seeking out new information, and being open to changing one’s beliefs based on new evidence.

What are the basics of scientific thinking?

The basics of scientific thinking include observation, hypothesis formation, experimentation, and analysis of data. Scientific thinking involves being systematic, objective, and logical in one’s approach to problem-solving. It is also important to be aware of one’s biases and assumptions when conducting scientific research.

What are the components of scientific and critical thinking?

The components of scientific and critical thinking include observation, analysis, interpretation, evaluation, and communication. These components are interconnected and involve being systematic, objective, and logical in one’s approach to problem-solving. Scientific and critical thinking also involve being open-minded, questioning assumptions, and seeking out new information.

How does critical thinking relate to scientific thinking?

Critical thinking is closely related to scientific thinking as both involve being systematic, objective, and logical in one’s approach to problem-solving. However, critical thinking can be applied to a wider range of topics beyond scientific research. Critical thinking involves evaluating arguments, analyzing evidence, and making informed decisions based on logical reasoning.

What are the three central components of scientific critical thinking?

The three central components of scientific critical thinking are skepticism, objectivity, and curiosity. Skepticism involves questioning assumptions and being open to changing one’s beliefs based on new evidence. Objectivity involves being unbiased and minimizing personal biases and assumptions when conducting research. Curiosity involves being open to new ideas and seeking out new information to expand one’s understanding of the world.

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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.

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Ideas to Action (i2a)

• Paul-Elder Critical Thinking Framework

Critical thinking is that mode of thinking – about any subject, content, or problem — in which the thinker improves the quality of his or her thinking by skillfully taking charge of the structures inherent in thinking and imposing intellectual standards upon them. (Paul and Elder, 2001). The Paul-Elder framework has three components:

• The elements of thought (reasoning)
• The  intellectual standards that should be applied to the elements of reasoning
• The intellectual traits associated with a cultivated critical thinker that result from the consistent and disciplined application of the intellectual standards to the elements of thought

According to Paul and Elder (1997), there are two essential dimensions of thinking that students need to master in order to learn how to upgrade their thinking. They need to be able to identify the "parts" of their thinking, and they need to be able to assess their use of these parts of thinking.

Elements of Thought (reasoning)

The "parts" or elements of thinking are as follows:

• All reasoning has a purpose
• All reasoning is an attempt to figure something out, to settle some question, to solve some problem
• All reasoning is based on assumptions
• All reasoning is done from some point of view
• All reasoning is based on data, information and evidence
• All reasoning is expressed through, and shaped by, concepts and ideas
• All reasoning contains inferences or interpretations by which we draw conclusions and give meaning to data
• All reasoning leads somewhere or has implications and consequences

Universal Intellectual Standards

The intellectual standards that are to these elements are used to determine the quality of reasoning. Good critical thinking requires having a command of these standards. According to Paul and Elder (1997 ,2006), the ultimate goal is for the standards of reasoning to become infused in all thinking so as to become the guide to better and better reasoning. The intellectual standards include:

Intellectual Traits

Consistent application of the standards of thinking to the elements of thinking result in the development of intellectual traits of:

• Intellectual Humility
• Intellectual Courage
• Intellectual Empathy
• Intellectual Autonomy
• Intellectual Integrity
• Intellectual Perseverance
• Confidence in Reason
• Fair-mindedness

Characteristics of a Well-Cultivated Critical Thinker

Habitual utilization of the intellectual traits produce a well-cultivated critical thinker who is able to:

• Raise vital questions and problems, formulating them clearly and precisely
• Gather and assess relevant information, using abstract ideas to interpret it effectively
• Come to well-reasoned conclusions and solutions, testing them against relevant criteria and standards;
• Think open-mindedly within alternative systems of thought, recognizing and assessing, as need be, their assumptions, implications, and practical consequences; and
• Communicate effectively with others in figuring out solutions to complex problems

Paul, R. and Elder, L. (2010). The Miniature Guide to Critical Thinking Concepts and Tools. Dillon Beach: Foundation for Critical Thinking Press.

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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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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.

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3 Core Critical Thinking Skills Every Thinker Should Have

Critically thinking about critical thinking skills..

Posted March 13, 2020 | Reviewed by Ekua Hagan

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I recently received an email from an educator friend, asking me to briefly describe the skills necessary for critical thinking. They were happy to fill in the blanks themselves from outside reading but wanted to know what specific skills they should focus on teaching their students. I took this as a good opportunity to dedicate a post here to such discussion, in order to provide my friend and any other interested parties with an overview.

To understand critical thinking skills and how they factor into critical thinking, one first needs a definition of the latter. Critical thinking (CT) is a metacognitive process, consisting of a number of skills and dispositions, that when used through self-regulatory reflective judgment, increases the chances of producing a logical conclusion to an argument or solution to a problem (Dwyer, 2017; Dwyer, Hogan & Stewart, 2014). On the surface, this definition clarifies two issues. First, critical thinking is metacognitive—simply, it requires the individual to think about thinking; second, its main components are reflective judgment, dispositions, and skills.

Below the surface, this description requires clarification; hence the impetus for this entry—what is meant by reflective judgment, disposition towards CT, and CT skills? Reflective judgment (i.e. an individuals' understanding of the nature, limits, and certainty of knowing and how this can affect their judgments [King & Kitchener, 1994]) and disposition towards CT (i.e. an inclination, tendency or willingness to perform a given thinking skill [Dwyer, 2017; Facione, Facione & Giancarlo, 1997; Ku, 2009; Norris, 1992; Siegel, 1999; Valenzuela, Nieto & Saiz, 2011]) have both already been covered in my posts; so, consistent with the aim of this piece, let’s discuss CT skills.

CT skills allow individuals to transcend lower-order, memorization-based learning strategies to gain a more complex understanding of the information or problems they encounter (Halpern, 2014). Though debate is ongoing over the definition of CT, one list stands out as a reasonable consensus conceptualization of CT skills. In 1988, a committee of 46 experts in the field of CT gathered to discuss CT conceptualisations, resulting in the Delphi Report; within which was overwhelmingly agreement (i.e. 95% consensus) that analysis , evaluation and inference were the core skills necessary for CT (Facione, 1990). Indeed, over 30 years later, these three CT skills remain the most commonly cited.

1. Analysis

Analysis is a core CT skill used to identify and examine the structure of an argument, the propositions within an argument and the role they play (e.g. the main conclusion, the premises and reasons provided to support the conclusion, objections to the conclusion and inferential relationships among propositions), as well as the sources of the propositions (e.g. personal experience, common belief, and research).

When it comes to analysing the basis for a standpoint, the structure of the argument can be extracted for subsequent evaluation (e.g. from dialogue and text). This can be accomplished through looking for propositions that either support or refute the central claim or other reasons and objections. Through analysis, the argument’s hierarchical structure begins to appear. Notably, argument mapping can aid the visual representation of this hierarchical structure and is supported by research as having positive effects on critical thinking (Butchart et al., 2009; Dwyer, 2011; Dwyer, Hogan & Stewart, 2012; van Gelder, Bisset & Cumming, 2004).

2. Evaluation

Evaluation is a core CT skill that is used in the assessment of propositions and claims (identified through the previous analysis ) with respect to their credibility; relevance; balance, bias (and potential omissions); as well as the logical strength amongst propositions (i.e. the strength of the inferential relationships). Such assessment allows for informed judgment regarding the overall strength or weakness of an argument (Dwyer, 2017; Facione, 1990). If an argument (or its propositions) is not credible, relevant, logical, and unbiased, you should consider excluding it or discussing its weaknesses as an objection.

Evaluating the credibility of claims and arguments involves progressing beyond merely identifying the source of propositions in an argument, to actually examining the "trustworthiness" of those identified sources (e.g. personal experiences, common beliefs/opinions, expert/authority opinion and scientific evidence). This is particularly important because some sources are more credible than others. Evaluation also implies deep consideration of the relevance of claims within an argument, which is accomplished by assessing the contextual relevance of claims and premises—that is, the pertinence or applicability of one proposition to another.

With respect to balance, bias (and potential omissions), it's important to consider the "slant" of an argument—if it seems imbalanced in favour of one line of thinking, then it’s quite possible that the argument has omitted key, opposing points that should also be considered. Imbalance may also imply some level of bias in the argument—another factor that should also be assessed.

However, just because an argument is balanced does not mean that it isn’t biased. It may very well be the case that the "opposing views" presented have been "cherry-picked" because they are easily disputed (akin to building a strawman ); thus, making supporting reasons appear stronger than they may actually be—and this is just one example of how a balanced argument may, in fact, be biased. The take-home message regarding balance, bias, and potential omissions should be that, in any argument, you should construct an understanding of the author or speaker’s motivations and consider how these might influence the structure and contents of the argument.

Finally, evaluating the logical strength of an argument is accomplished through monitoring both the logical relationships amongst propositions and the claims they infer. Assessment of logical strength can actually be aided through subsequent inference, as a means of double-checking the logical strength. For example, this can be checked by asking whether or not a particular proposition can actually be inferred based on the propositions that precede it. A useful means of developing this sub-skill is through practicing syllogistic reasoning .

3. Inference

Similar to other educational concepts like synthesis (e.g., see Bloom et al., 1956; Dwyer, 2011; 2017), the final core CT skill, inference , involves the “gathering” of credible, relevant and logical evidence based on the previous analysis and evaluation, for the purpose of drawing a reasonable conclusion (Dwyer, 2017; Facione, 1990). Drawing a conclusion always implies some act of synthesis (i.e. the ability to put parts of information together to form a new whole; see Dwyer, 2011). However, inference is a unique form of synthesis in that it involves the formulation of a set of conclusions derived from a series of arguments or a body of evidence. This inference may imply accepting a conclusion pointed to by an author in light of the evidence they present, or "conjecturing an alternative," equally logical, conclusion or argument based on the available evidence (Facione, 1990). The ability to infer a conclusion in this manner can be completed through formal logic strategies, informal logic strategies (or both) in order to derive intermediate conclusions, as well as central claims.

Another important aspect of inference involves the querying of available evidence, for example, by recognising the need for additional information, gathering it and judging the plausibility of utilising such information for the purpose of drawing a conclusion. Notably, in the context of querying evidence and conjecturing alternative conclusions, inference overlaps with evaluation to a certain degree in that both skills are used to judge the relevance and acceptability of a claim or argument. Furthermore, after inferring a conclusion, the resulting argument should be re-evaluated to ensure that it is reasonable to draw the conclusion that was derived.

Overall, the application of critical thinking skills is a process—one must analyse, evaluate and then infer; and this process can be repeated to ensure that a reasonable conclusion has been drawn. In an effort to simplify the description of this process, for the past few years, I’ve used the analogy of picking apples for baking . We begin by picking apples from a tree. Consider the tree as an analogy, in its own right, for an argument, which is often hierarchically structured like a tree-diagram. By picking apples, I mean identifying propositions and the role they play (i.e. analysis). Once we pick an apple, we evaluate it—we make sure it isn’t rotten (i.e. lacks credibility, is biased) and is suitable for baking (i.e. relevant and logically strong). Finally, we infer— we gather the apples in a basket and bring them home and group them together based on some rationale for construction— maybe four for a pie, three for a crumble and another four for a tart. By the end of the process, we have baked some apple-based goods, or developed a conclusion, solution or decision through critical thinking.

Of course, there is more to critical thinking than the application of skills—a critical thinker must also have the disposition to think critically and engage reflective judgment. However, without the appropriate skills—analysis, evaluation, and inference, it is not likely that CT will be applied. For example, though one might be willing to use CT skills and engage reflective judgment, they may not know how to do so. Conversely, though one might be aware of which CT skills to use in a given context and may have the capacity to perform well when using these skills, they may not be disposed to use them (Valenzuela, Nieto & Saiz, 2011). Though the core CT skills of analysis, evaluation, and inference are not the only important aspects of CT, they are essential for its application.

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Dwyer, C.P., Hogan, M.J., & Stewart, I. (2012). An evaluation of argument mapping as a method of enhancing critical thinking performance in e-learning environments. Metacognition and Learning, 7, 219-244.

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Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that threatens to derail our entire day. Here’s how we can face our triggers with less reactivity so that we can get on with our lives.

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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
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*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.

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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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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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Fostering Scientific Literacy and Critical Thinking in Elementary Science Education

• Published: 30 December 2014
• Volume 14 , pages 659–680, ( 2016 )

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• Rui Marques Vieira 1 , 2 &
• Celina Tenreiro-Vieira 2  

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Scientific literacy (SL) and critical thinking (CT) are key components of science education aiming to prepare students to think and to function as responsible citizens in a world increasingly affected by science and technology (S&T). Therefore, students should be given opportunities in their science classes to be engaged in learning experiences that promote SL and CT, which may trigger the need to build and develop knowledge, attitudes/values, thinking abilities, and standards/criteria in an integrated way, resulting in their ability to know how to take responsible action in contexts and situations of personal and social relevance. This paper reports on a study to design, implement, and assess science learning experiences focused on CT toward SL goal. Results support the conclusion that the learning experiences developed and implemented in a grade 6 science classroom had a significant influence on the students’ CT and SL. Within this elementary school context, the theoretical framework used appears to be a relevant and practical aid for developing learning experiences that promote CT/SL and in supporting teaching practices that are more in line with the goals of critical scientific literacy.

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The authors thank Dr. Larry Yore and Mrs. Sharyl Yore for their mentoring assistance.

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Vieira, R.M., Tenreiro-Vieira, C. Fostering Scientific Literacy and Critical Thinking in Elementary Science Education. Int J of Sci and Math Educ 14 , 659–680 (2016). https://doi.org/10.1007/s10763-014-9605-2

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Received : 30 April 2014

Accepted : 13 November 2014

Published : 30 December 2014

Issue Date : May 2016

DOI : https://doi.org/10.1007/s10763-014-9605-2

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