hypothesis in experimental psychology

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © 2022 by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.4 Developing a Hypothesis

Learning objectives.

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 4.4 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Understanding Experimental Hypotheses in Psychology

Have you ever wondered what an experimental hypothesis is and why it is crucial in psychology research? In this article, we will explore the components of an experimental hypothesis, the different types, and how it is formulated. We will also discuss the role of an experimental hypothesis in research design and how it is tested. By the end, you will have a clear understanding of the importance of experimental hypotheses and the possible outcomes they can lead to.

1 What Is an Experimental Hypothesis?
2 Why Is an Experimental Hypothesis Important in Psychology?
3.1 Independent Variable
3.2 Dependent Variable
3.3 Control Variables
4.1 Directional Hypothesis
4.2 Non-directional Hypothesis
4.3 Null Hypothesis
5.1 Review of Literature
5.2 Identifying Variables
5.3 Making Predictions
6 What Is the Role of an Experimental Hypothesis in Research Design?
7.1 Choosing a Research Design
7.2 Collecting Data
7.3 Analyzing Data
8 What Are the Possible Outcomes of an Experimental Hypothesis?
9.1 What is an experimental hypothesis in psychology?
9.2 How is an experimental hypothesis different from a research question?
9.3 What are the characteristics of a good experimental hypothesis?
9.4 Can an experimental hypothesis be proven to be true?
9.5 Why is it important to have a well-formed experimental hypothesis?
9.6 Can an experimental hypothesis change during the course of a study?

What Is an Experimental Hypothesis?

An experimental hypothesis in psychology is a statement that proposes a relationship between variables and is essential for guiding empirical research and testing theories.

It serves as the foundation of scientific investigations, providing a clear direction for researchers to validate or refute concepts through systematic experimentation. By formulating specific predictions about the expected outcomes of an experiment, the hypothesis establishes the framework for data collection and analysis. It helps researchers in shaping their experimental design to ensure the validity and reliability of their findings. In the field of psychology, experimental hypotheses play a crucial role in unraveling complex human behaviors and mental processes, offering insights into the underlying mechanisms driving various psychological phenomena.

Why Is an Experimental Hypothesis Important in Psychology?

Understanding the importance of an experimental hypothesis in psychology is crucial as it serves as the foundation for empirical research, theory testing, and deriving logical conclusions based on observable behavior and phenomena.

An experimental hypothesis plays a key role in guiding the research process by outlining the specific relationship or effect that researchers aim to investigate. By formulating a clear hypothesis, researchers establish a framework for their studies, enabling them to design experiments, collect data, and analyze results effectively.

Hypotheses in psychology contribute to the development and refinement of theories by providing researchers with a means to test and validate their conceptual ideas through systematic investigation. This systematic approach not only adds credibility to psychological theories but also fosters a continuous cycle of theory development and refinement based on empirical evidence.

What Are the Components of an Experimental Hypothesis?

The components of an experimental hypothesis include the independent variable, dependent variable, and control variables, each defined by operational definitions to ensure clarity and replicability in research.

An independent variable is the factor that is manipulated or changed by the researcher to observe its effect on the dependent variable. The operational definition of the independent variable specifies how it will be measured or manipulated in the study, ensuring consistency and objectivity.

On the other hand, the dependent variable is what is being measured or observed in response to the changes in the independent variable, providing the data for analysis and drawing conclusions.

Control variables are the factors kept constant to prevent their influence on the relationship between the independent and dependent variables, maintaining the integrity of the experiment. By establishing controlled conditions, researchers can minimize external influences and confidently attribute any observed changes to the manipulated independent variable.

Independent Variable

The independent variable in an experimental hypothesis is the factor that researchers manipulate to observe its effect on the dependent variable, aiming to establish causal relationships between variables.

By deliberately changing or controlling the independent variable, researchers can assess how this alteration impacts the dependent variable, which is the outcome or response that is measured. This process of manipulating the independent variable and observing its influence allows researchers to draw conclusions about the causal relationship between the two variables. It is essential in experimental research as it helps determine whether changes in the independent variable directly lead to changes in the dependent variable, thus contributing to the understanding of cause and effect in scientific investigations.

Dependent Variable

The dependent variable in an experimental hypothesis is the outcome or behavior that is measured to assess the impact of changes in the independent variable, helping researchers understand the effects of manipulations.

Dependent variables play a crucial role in scientific investigations as they provide a reliable measure of how alterations in the independent factor influence the subjects under study. By analyzing the responses exhibited by the dependent variable, researchers can draw conclusions about the causal relationships present within the experimental setup. Quantifying and evaluating these behavioral or outcome-based changes allows for a systematic evaluation of the variables at play in the research design.

Control Variables

Control variables in an experimental hypothesis are factors that are kept constant throughout the study to isolate the effects of the independent variable, ensuring reliability, replicability, and accurate conclusions.

By maintaining these control variables constant, researchers can minimize the impact of extraneous variables, thus reducing the likelihood of confounding factors that could skew the results. This meticulous attention to detail not only enhances the internal validity of the study but also facilitates the process of replication by providing a clear framework for others to follow. Through the careful manipulation of these variables, scientists can establish a causal relationship between the independent and dependent variables, strengthening the overall robustness and generalizability of the findings.

What Are the Types of Experimental Hypotheses?

Experimental hypotheses can be categorized into directional hypotheses that predict specific outcomes, non-directional hypotheses that suggest a relationship without specifying direction, and null hypotheses that propose no effect between variables.

Directional hypotheses are characterized by their clear predictions of the expected relationship between variables. For example, a directional hypothesis could state that ‘increased sunlight exposure will lead to higher plant growth.’ On the other hand, non-directional hypotheses indicate a relationship between variables without specifying the nature of the relationship. An example of a non-directional hypothesis could be ‘there is a correlation between coffee consumption and alertness.’ Null hypotheses, in contrast, assert that there is no significant effect or relationship between the variables under study. For instance, a null hypothesis could be ‘there is no difference in test scores between students who study in silence and those who listen to music while studying.’

Directional Hypothesis

A directional hypothesis in experimental research predicts the direction of the relationship between variables, indicating an expected outcome based on prior knowledge or theory.

By formulating a directional hypothesis, researchers make a clear statement about the relationship they expect to find in their study. This hypothesis serves as a roadmap, guiding the research process by defining the specific outcome the study aims to confirm or refute. It helps focus the study’s objectives, methodology, and data analysis. A directional hypothesis plays a crucial role in guiding the interpretation of results, enabling researchers to draw meaningful conclusions based on whether the expected outcome was supported or not.

Non-directional Hypothesis

A non-directional hypothesis in experimental studies suggests a relationship between variables without specifying the nature or direction of that relationship, allowing for exploratory research and open-ended conclusions.

By omitting the direction of the relationship between variables, a non-directional hypothesis encourages researchers to keep an open mind during the investigation process. This type of hypothesis is particularly useful when exploring new or complex phenomena where the exact nature of the relationship is not yet well understood. It allows for flexibility in data interpretation, enabling the researcher to consider various possibilities without being constrained by predefined expectations.

Null Hypothesis

The null hypothesis states that there is no significant effect or relationship between variables, serving as a benchmark for comparison in statistical testing and hypothesis evaluation.

When researchers design a study, they set up the null hypothesis to represent the default position that there is no difference or effect. By establishing this baseline, researchers can then test whether the results obtained significantly deviate from what would be expected by chance alone. This process allows for the rejection or acceptance of the null hypothesis, which in turn guides the interpretation of research findings and conclusions drawn from the study.

How Is an Experimental Hypothesis Formulated?

Formulating an experimental hypothesis involves conducting a thorough review of literature, identifying relevant variables, and making predictions about the expected outcomes based on existing knowledge.

This initial phase of the scientific process is crucial for setting the groundwork for an experiment that aims to test a specific hypothesis. By looking into existing research and theories in the field, researchers can establish a solid foundation for their own study. This literature review helps in understanding the current state of knowledge, gaps in research, and potential areas for exploration.

Identifying the key variables that play a role in the phenomenon under investigation is another critical step. These variables are the factors that are manipulated, controlled, or measured during the experiment to assess their impact on the outcome.

Review of Literature

A literature review is crucial in formulating an experimental hypothesis as it provides insights, background information, and context for developing research questions and identifying variables.

By looking into existing studies and scholarly articles, researchers can gain a comprehensive understanding of the topic under investigation. This, in turn, aids in formulating specific research questions that address gaps in knowledge and help in the selection of appropriate variables. The literature review is like the foundation upon which a solid research hypothesis is built, ensuring that the study’s objectives are clear, relevant, and based on a thorough examination of previous literature.

Identifying Variables

Identifying variables is a critical step in experimental hypothesis formulation as it helps establish causal relationships, determine research focus, and define the scope of the study.

When identifying variables, researchers carefully select factors that they believe can affect the outcome of the study. These variables can be independent, dependent, or controlled, with each playing a unique role in the research design. The selection process involves considering past literature, theoretical frameworks, and the overall research question to ensure that the chosen variables align with the study objectives.

Variables serve as the building blocks of hypotheses, representing the key components that drive the intended outcomes and allow researchers to test specific relationships or effects. Understanding the significance of variables in hypothesis development is crucial for advancing scientific inquiry and making informed conclusions based on empirical data.

Making Predictions

Making predictions about expected outcomes is an essential aspect of formulating an experimental hypothesis, guiding research design, data collection, and analysis methods.

By generating hypotheses with clear predictions, researchers establish a roadmap for their studies, setting parameters for investigation and providing a focus for data collection. Predictions help ensure that experiments are structured to test specific hypotheses rigorously, allowing researchers to draw reliable conclusions from the data. They serve as a compass in the research journey, guiding the selection of variables, controls, and statistical analyses to uncover meaningful insights. Accurate predictions enhance the reproducibility and credibility of scientific findings, contributing to the overall advancement of knowledge in various fields.

What Is the Role of an Experimental Hypothesis in Research Design?

An experimental hypothesis plays a pivotal role in research design by providing a framework for conducting empirical studies, selecting variables, and establishing the basis for drawing conclusions.

When crafting an experimental hypothesis, researchers embark on a journey to explore the underlying relationships between variables, with the aim of testing specific predictions and hypotheses. This process involves careful consideration of the research question, the variables involved, and the anticipated outcomes. By outlining the expected cause-and-effect relationships, researchers set the stage for the rest of the study, guiding the selection of appropriate methodologies and data collection techniques to ensure a rigorous investigation.

How Is an Experimental Hypothesis Tested?

Testing an experimental hypothesis involves choosing an appropriate research design, collecting relevant data, and analyzing the results to evaluate the hypothesis’s validity and draw meaningful conclusions.

When selecting a research design, researchers must determine whether to conduct an observational study, an experiment, a survey, or a combination of these methods.

Data collection methods can vary widely, from direct observations and interviews to using existing datasets or conducting experiments in controlled environments.

Once the data is gathered, meticulous data analysis is imperative to uncover patterns, relationships, and trends that can provide insights into the hypothesis being tested.

The significance of data analysis cannot be overstated, as it is the stage where researchers can validate or refute their initial assumptions by interpreting the collected information objectively. For a better understanding of experimental hypotheses in psychology, visit Understanding Experimental Hypotheses in Psychology .

Choosing a Research Design

Selecting a research design is a crucial step in testing an experimental hypothesis as it determines the structure, methodology, and controls necessary to establish causal relationships between variables.

By choosing the appropriate research design, researchers can ensure that their study is set up in a way that allows them to draw valid conclusions about the effects of the independent variable on the dependent variable. A well-chosen research design helps in reducing biases, controlling extraneous variables, and establishing a clear cause-and-effect relationship, which is essential for supporting or refuting the hypothesis being tested. The research design also impacts the validity of the results obtained, influencing the generalizability and reliability of the findings.

Collecting Data

Collecting data in experimental research involves gathering information from participants, observing behavior, and recording outcomes to analyze the effects of the independent variable on the dependent variable.

Participant involvement plays a crucial role in providing researchers with the necessary data to draw meaningful conclusions. Through carefully designed experiments, researchers can assess how changes in the independent variable influence the dependent variable.

This process often includes meticulous behavioral observations to capture real-time responses and interactions. Subsequently, recording these observations is essential for accurate data analysis and interpretation. Researchers must ensure systematic and thorough documentation of outcomes to maintain the integrity and validity of the study results. The accuracy and reliability of the collected data are paramount in deriving valid conclusions about the research hypothesis.

Analyzing Data

Analyzing data from experimental studies is essential for evaluating the results, assessing the impact of variables, and determining the significance of the experimental hypothesis in relation to the observed outcomes.

Data analysis plays a crucial role in the scientific method, providing a systematic approach to uncover patterns, trends, and relationships within the collected data. By utilizing statistical tools such as regression analysis, ANOVA, and t-tests , researchers can quantitatively measure the strength of the evidence supporting or refuting the experimental hypothesis. This process not only aids in result interpretation but also guides researchers in drawing meaningful conclusions and determining the validity of their hypotheses.

What Are the Possible Outcomes of an Experimental Hypothesis?

Experimental hypotheses can lead to various outcomes, including supporting or refuting theories, deriving conclusions based on data analysis, and contributing to the understanding of phenomena through controlled experimentation.

When experimental hypotheses are tested and results are analyzed, researchers can gain valuable insights into the mechanisms underlying certain behaviors or phenomena. By generating empirical evidence, these hypotheses help shape scientific theories and expand the boundaries of knowledge in different fields. Successful experiments can lead to the formulation of new questions and avenues for further exploration, sparking curiosity and advancement in research. The impact of experimental hypotheses extends far beyond individual studies, influencing the broader scientific community and shaping future investigations.

Frequently Asked Questions

What is an experimental hypothesis in psychology.

An experimental hypothesis in psychology is a statement that predicts the relationship between two or more variables in an experiment. It is the specific question or assertion that the researcher is testing in their study.

How is an experimental hypothesis different from a research question?

While an experimental hypothesis is a specific and testable statement, a research question is a broader and more general inquiry about a topic. An experimental hypothesis is used to guide the design and analysis of an experiment, while a research question can guide the overall research process.

What are the characteristics of a good experimental hypothesis?

A good experimental hypothesis is based on previous research and theory, is specific and testable, and includes clear and measurable variables. It should also be falsifiable, meaning that it can be proven false through the results of the experiment.

Can an experimental hypothesis be proven to be true?

No, an experimental hypothesis cannot be proven to be true. It can only be supported or rejected by the results of the experiment. This is because there may be other factors or variables that were not accounted for in the experiment that could affect the outcome.

Why is it important to have a well-formed experimental hypothesis?

A well-formed experimental hypothesis helps guide the design and analysis of an experiment, making the research process more systematic and organized. It also allows for clear communication of the purpose and expected outcomes of the study to other researchers and the general public.

Can an experimental hypothesis change during the course of a study?

Yes, an experimental hypothesis can change during the course of a study if new information or unexpected results are discovered. This is a normal part of the scientific process and can lead to new and important findings. However, any changes to the experimental hypothesis should be clearly stated and justified.

Ethan Clarke holds a Master’s in Organizational Psychology and has spent years consulting for Fortune 500 companies. His expertise in workplace behavior and employee well-being has led to significant organizational changes and improved company cultures. Ethan is passionate about applying psychological principles to enhance productivity and job satisfaction. Through his writing, he aims to bridge the gap between academic research and practical application in the workplace, providing readers with actionable insights for professional growth and organizational development.

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2.1 Psychologists Use the Scientific Method to Guide Their Research

Learning objectives.

Describe the principles of the scientific method and explain its importance in conducting and interpreting research.
Differentiate laws from theories and explain how research hypotheses are developed and tested.
Discuss the procedures that researchers use to ensure that their research with humans and with animals is ethical.

Psychologists aren’t the only people who seek to understand human behavior and solve social problems. Philosophers, religious leaders, and politicians, among others, also strive to provide explanations for human behavior. But psychologists believe that research is the best tool for understanding human beings and their relationships with others. Rather than accepting the claim of a philosopher that people do (or do not) have free will, a psychologist would collect data to empirically test whether or not people are able to actively control their own behavior. Rather than accepting a politician’s contention that creating (or abandoning) a new center for mental health will improve the lives of individuals in the inner city, a psychologist would empirically assess the effects of receiving mental health treatment on the quality of life of the recipients. The statements made by psychologists are empirical , which means they are based on systematic collection and analysis of data .

The Scientific Method

All scientists (whether they are physicists, chemists, biologists, sociologists, or psychologists) are engaged in the basic processes of collecting data and drawing conclusions about those data. The methods used by scientists have developed over many years and provide a common framework for developing, organizing, and sharing information. The scientific method is the set of assumptions, rules, and procedures scientists use to conduct research .

In addition to requiring that science be empirical, the scientific method demands that the procedures used be objective , or free from the personal bias or emotions of the scientist . The scientific method proscribes how scientists collect and analyze data, how they draw conclusions from data, and how they share data with others. These rules increase objectivity by placing data under the scrutiny of other scientists and even the public at large. Because data are reported objectively, other scientists know exactly how the scientist collected and analyzed the data. This means that they do not have to rely only on the scientist’s own interpretation of the data; they may draw their own, potentially different, conclusions.

Most new research is designed to replicate —that is, to repeat, add to, or modify—previous research findings. The scientific method therefore results in an accumulation of scientific knowledge through the reporting of research and the addition to and modifications of these reported findings by other scientists.

Laws and Theories as Organizing Principles

One goal of research is to organize information into meaningful statements that can be applied in many situations. Principles that are so general as to apply to all situations in a given domain of inquiry are known as laws . There are well-known laws in the physical sciences, such as the law of gravity and the laws of thermodynamics, and there are some universally accepted laws in psychology, such as the law of effect and Weber’s law. But because laws are very general principles and their validity has already been well established, they are themselves rarely directly subjected to scientific test.

The next step down from laws in the hierarchy of organizing principles is theory. A theory is an integrated set of principles that explains and predicts many, but not all, observed relationships within a given domain of inquiry . One example of an important theory in psychology is the stage theory of cognitive development proposed by the Swiss psychologist Jean Piaget. The theory states that children pass through a series of cognitive stages as they grow, each of which must be mastered in succession before movement to the next cognitive stage can occur. This is an extremely useful theory in human development because it can be applied to many different content areas and can be tested in many different ways.

Good theories have four important characteristics. First, good theories are general , meaning they summarize many different outcomes. Second, they are parsimonious , meaning they provide the simplest possible account of those outcomes. The stage theory of cognitive development meets both of these requirements. It can account for developmental changes in behavior across a wide variety of domains, and yet it does so parsimoniously—by hypothesizing a simple set of cognitive stages. Third, good theories provide ideas for future research . The stage theory of cognitive development has been applied not only to learning about cognitive skills, but also to the study of children’s moral (Kohlberg, 1966) and gender (Ruble & Martin, 1998) development.

Finally, good theories are falsifiable (Popper, 1959), which means the variables of interest can be adequately measured and the relationships between the variables that are predicted by the theory can be shown through research to be incorrect . The stage theory of cognitive development is falsifiable because the stages of cognitive reasoning can be measured and because if research discovers, for instance, that children learn new tasks before they have reached the cognitive stage hypothesized to be required for that task, then the theory will be shown to be incorrect.

No single theory is able to account for all behavior in all cases. Rather, theories are each limited in that they make accurate predictions in some situations or for some people but not in other situations or for other people. As a result, there is a constant exchange between theory and data: Existing theories are modified on the basis of collected data, and the new modified theories then make new predictions that are tested by new data, and so forth. When a better theory is found, it will replace the old one. This is part of the accumulation of scientific knowledge.

The Research Hypothesis

Theories are usually framed too broadly to be tested in a single experiment. Therefore, scientists use a more precise statement of the presumed relationship among specific parts of a theory—a research hypothesis—as the basis for their research. A research hypothesis is a specific and falsifiable prediction about the relationship between or among two or more variables , where a variable is any attribute that can assume different values among different people or across different times or places . The research hypothesis states the existence of a relationship between the variables of interest and the specific direction of that relationship. For instance, the research hypothesis “Using marijuana will reduce learning” predicts that there is a relationship between a variable “using marijuana” and another variable called “learning.” Similarly, in the research hypothesis “Participating in psychotherapy will reduce anxiety,” the variables that are expected to be related are “participating in psychotherapy” and “level of anxiety.”

When stated in an abstract manner, the ideas that form the basis of a research hypothesis are known as conceptual variables. Conceptual variables are abstract ideas that form the basis of research hypotheses . Sometimes the conceptual variables are rather simple—for instance, “age,” “gender,” or “weight.” In other cases the conceptual variables represent more complex ideas, such as “anxiety,” “cognitive development,” “learning,” self-esteem,” or “sexism.”

The first step in testing a research hypothesis involves turning the conceptual variables into measured variables , which are variables consisting of numbers that represent the conceptual variables . For instance, the conceptual variable “participating in psychotherapy” could be represented as the measured variable “number of psychotherapy hours the patient has accrued” and the conceptual variable “using marijuana” could be assessed by having the research participants rate, on a scale from 1 to 10, how often they use marijuana or by administering a blood test that measures the presence of the chemicals in marijuana.

Psychologists use the term operational definition to refer to a precise statement of how a conceptual variable is turned into a measured variable . The relationship between conceptual and measured variables in a research hypothesis is diagrammed in Figure 2.1 “Diagram of a Research Hypothesis” . The conceptual variables are represented within circles at the top of the figure, and the measured variables are represented within squares at the bottom. The two vertical arrows, which lead from the conceptual variables to the measured variables, represent the operational definitions of the two variables. The arrows indicate the expectation that changes in the conceptual variables (psychotherapy and anxiety in this example) will cause changes in the corresponding measured variables. The measured variables are then used to draw inferences about the conceptual variables.

Figure 2.1 Diagram of a Research Hypothesis

In this research hypothesis, the conceptual variable of attending psychotherapy is operationalized using the number of hours of psychotherapy the client has completed, and the conceptual variable of anxiety is operationalized using self-reported levels of anxiety. The research hypothesis is that more psychotherapy will be related to less reported anxiety.

Table 2.1 “Examples of the Operational Definitions of Conceptual Variables That Have Been Used in Psychological Research” lists some potential operational definitions of conceptual variables that have been used in psychological research. As you read through this list, note that in contrast to the abstract conceptual variables, the measured variables are very specific. This specificity is important for two reasons. First, more specific definitions mean that there is less danger that the collected data will be misunderstood by others. Second, specific definitions will enable future researchers to replicate the research.

Table 2.1 Examples of the Operational Definitions of Conceptual Variables That Have Been Used in Psychological Research

Conducting Ethical Research

One of the questions that all scientists must address concerns the ethics of their research. Physicists are concerned about the potentially harmful outcomes of their experiments with nuclear materials. Biologists worry about the potential outcomes of creating genetically engineered human babies. Medical researchers agonize over the ethics of withholding potentially beneficial drugs from control groups in clinical trials. Likewise, psychologists are continually considering the ethics of their research.

Research in psychology may cause some stress, harm, or inconvenience for the people who participate in that research. For instance, researchers may require introductory psychology students to participate in research projects and then deceive these students, at least temporarily, about the nature of the research. Psychologists may induce stress, anxiety, or negative moods in their participants, expose them to weak electrical shocks, or convince them to behave in ways that violate their moral standards. And researchers may sometimes use animals in their research, potentially harming them in the process.

Decisions about whether research is ethical are made using established ethical codes developed by scientific organizations, such as the American Psychological Association, and federal governments. In the United States, the Department of Health and Human Services provides the guidelines for ethical standards in research. Some research, such as the research conducted by the Nazis on prisoners during World War II, is perceived as immoral by almost everyone. Other procedures, such as the use of animals in research testing the effectiveness of drugs, are more controversial.

Scientific research has provided information that has improved the lives of many people. Therefore, it is unreasonable to argue that because scientific research has costs, no research should be conducted. This argument fails to consider the fact that there are significant costs to not doing research and that these costs may be greater than the potential costs of conducting the research (Rosenthal, 1994). In each case, before beginning to conduct the research, scientists have attempted to determine the potential risks and benefits of the research and have come to the conclusion that the potential benefits of conducting the research outweigh the potential costs to the research participants.

Characteristics of an Ethical Research Project Using Human Participants

Trust and positive rapport are created between the researcher and the participant.
The rights of both the experimenter and participant are considered, and the relationship between them is mutually beneficial.
The experimenter treats the participant with concern and respect and attempts to make the research experience a pleasant and informative one.
Before the research begins, the participant is given all information relevant to his or her decision to participate, including any possibilities of physical danger or psychological stress.
The participant is given a chance to have questions about the procedure answered, thus guaranteeing his or her free choice about participating.
After the experiment is over, any deception that has been used is made public, and the necessity for it is explained.
The experimenter carefully debriefs the participant, explaining the underlying research hypothesis and the purpose of the experimental procedure in detail and answering any questions.
The experimenter provides information about how he or she can be contacted and offers to provide information about the results of the research if the participant is interested in receiving it. (Stangor, 2011)

This list presents some of the most important factors that psychologists take into consideration when designing their research. The most direct ethical concern of the scientist is to prevent harm to the research participants. One example is the well-known research of Stanley Milgram (1974) investigating obedience to authority. In these studies, participants were induced by an experimenter to administer electric shocks to another person so that Milgram could study the extent to which they would obey the demands of an authority figure. Most participants evidenced high levels of stress resulting from the psychological conflict they experienced between engaging in aggressive and dangerous behavior and following the instructions of the experimenter. Studies such as those by Milgram are no longer conducted because the scientific community is now much more sensitized to the potential of such procedures to create emotional discomfort or harm.

Another goal of ethical research is to guarantee that participants have free choice regarding whether they wish to participate in research. Students in psychology classes may be allowed, or even required, to participate in research, but they are also always given an option to choose a different study to be in, or to perform other activities instead. And once an experiment begins, the research participant is always free to leave the experiment if he or she wishes to. Concerns with free choice also occur in institutional settings, such as in schools, hospitals, corporations, and prisons, when individuals are required by the institutions to take certain tests, or when employees are told or asked to participate in research.

Researchers must also protect the privacy of the research participants. In some cases data can be kept anonymous by not having the respondents put any identifying information on their questionnaires. In other cases the data cannot be anonymous because the researcher needs to keep track of which respondent contributed the data. In this case one technique is to have each participant use a unique code number to identify his or her data, such as the last four digits of the student ID number. In this way the researcher can keep track of which person completed which questionnaire, but no one will be able to connect the data with the individual who contributed them.

Perhaps the most widespread ethical concern to the participants in behavioral research is the extent to which researchers employ deception. Deception occurs whenever research participants are not completely and fully informed about the nature of the research project before participating in it . Deception may occur in an active way, such as when the researcher tells the participants that he or she is studying learning when in fact the experiment really concerns obedience to authority. In other cases the deception is more passive, such as when participants are not told about the hypothesis being studied or the potential use of the data being collected.

Some researchers have argued that no deception should ever be used in any research (Baumrind, 1985). They argue that participants should always be told the complete truth about the nature of the research they are in, and that when participants are deceived there will be negative consequences, such as the possibility that participants may arrive at other studies already expecting to be deceived. Other psychologists defend the use of deception on the grounds that it is needed to get participants to act naturally and to enable the study of psychological phenomena that might not otherwise get investigated. They argue that it would be impossible to study topics such as altruism, aggression, obedience, and stereotyping without using deception because if participants were informed ahead of time what the study involved, this knowledge would certainly change their behavior. The codes of ethics of the American Psychological Association and other organizations allow researchers to use deception, but these codes also require them to explicitly consider how their research might be conducted without the use of deception.

Ensuring That Research Is Ethical

Making decisions about the ethics of research involves weighing the costs and benefits of conducting versus not conducting a given research project. The costs involve potential harm to the research participants and to the field, whereas the benefits include the potential for advancing knowledge about human behavior and offering various advantages, some educational, to the individual participants. Most generally, the ethics of a given research project are determined through a cost-benefit analysis , in which the costs are compared to the benefits. If the potential costs of the research appear to outweigh any potential benefits that might come from it, then the research should not proceed.

Arriving at a cost-benefit ratio is not simple. For one thing, there is no way to know ahead of time what the effects of a given procedure will be on every person or animal who participates or what benefit to society the research is likely to produce. In addition, what is ethical is defined by the current state of thinking within society, and thus perceived costs and benefits change over time. The U.S. Department of Health and Human Services regulations require that all universities receiving funds from the department set up an Institutional Review Board (IRB) to determine whether proposed research meets department regulations. The Institutional Review Board (IRB) is a committee of at least five members whose goal it is to determine the cost-benefit ratio of research conducted within an institution . The IRB approves the procedures of all the research conducted at the institution before the research can begin. The board may suggest modifications to the procedures, or (in rare cases) it may inform the scientist that the research violates Department of Health and Human Services guidelines and thus cannot be conducted at all.

One important tool for ensuring that research is ethical is the use of informed consent . A sample informed consent form is shown in Figure 2.2 “Sample Consent Form” . Informed consent , conducted before a participant begins a research session, is designed to explain the research procedures and inform the participant of his or her rights during the investigation . The informed consent explains as much as possible about the true nature of the study, particularly everything that might be expected to influence willingness to participate, but it may in some cases withhold some information that allows the study to work.

Figure 2.2 Sample Consent Form

The informed consent form explains the research procedures and informs the participant of his or her rights during the investigation.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Because participating in research has the potential for producing long-term changes in the research participants, all participants should be fully debriefed immediately after their participation. The debriefing is a procedure designed to fully explain the purposes and procedures of the research and remove any harmful aftereffects of participation .

Research With Animals

Because animals make up an important part of the natural world, and because some research cannot be conducted using humans, animals are also participants in psychological research. Most psychological research using animals is now conducted with rats, mice, and birds, and the use of other animals in research is declining (Thomas & Blackman, 1992). As with ethical decisions involving human participants, a set of basic principles has been developed that helps researchers make informed decisions about such research; a summary is shown below.

APA Guidelines on Humane Care and Use of Animals in Research

The following are some of the most important ethical principles from the American Psychological Association’s guidelines on research with animals.

Psychologists acquire, care for, use, and dispose of animals in compliance with current federal, state, and local laws and regulations, and with professional standards.
Psychologists trained in research methods and experienced in the care of laboratory animals supervise all procedures involving animals and are responsible for ensuring appropriate consideration of their comfort, health, and humane treatment.
Psychologists ensure that all individuals under their supervision who are using animals have received instruction in research methods and in the care, maintenance, and handling of the species being used, to the extent appropriate to their role.
Psychologists make reasonable efforts to minimize the discomfort, infection, illness, and pain of animal subjects.
Psychologists use a procedure subjecting animals to pain, stress, or privation only when an alternative procedure is unavailable and the goal is justified by its prospective scientific, educational, or applied value.
Psychologists perform surgical procedures under appropriate anesthesia and follow techniques to avoid infection and minimize pain during and after surgery.
When it is appropriate that an animal’s life be terminated, psychologists proceed rapidly, with an effort to minimize pain and in accordance with accepted procedures. (American Psychological Association, 2002)

Psychologists may use animals in their research, but they make reasonable efforts to minimize the discomfort the animals experience.

Because the use of animals in research involves a personal value, people naturally disagree about this practice. Although many people accept the value of such research (Plous, 1996), a minority of people, including animal-rights activists, believes that it is ethically wrong to conduct research on animals. This argument is based on the assumption that because animals are living creatures just as humans are, no harm should ever be done to them.

Most scientists, however, reject this view. They argue that such beliefs ignore the potential benefits that have and continue to come from research with animals. For instance, drugs that can reduce the incidence of cancer or AIDS may first be tested on animals, and surgery that can save human lives may first be practiced on animals. Research on animals has also led to a better understanding of the physiological causes of depression, phobias, and stress, among other illnesses. In contrast to animal-rights activists, then, scientists believe that because there are many benefits that accrue from animal research, such research can and should continue as long as the humane treatment of the animals used in the research is guaranteed.

Key Takeaways

Psychologists use the scientific method to generate, accumulate, and report scientific knowledge.
Basic research, which answers questions about behavior, and applied research, which finds solutions to everyday problems, inform each other and work together to advance science.
Research reports describing scientific studies are published in scientific journals so that other scientists and laypersons may review the empirical findings.
Organizing principles, including laws, theories and research hypotheses, give structure and uniformity to scientific methods.
Concerns for conducting ethical research are paramount. Researchers assure that participants are given free choice to participate and that their privacy is protected. Informed consent and debriefing help provide humane treatment of participants.
A cost-benefit analysis is used to determine what research should and should not be allowed to proceed.

Exercises and Critical Thinking

Give an example from personal experience of how you or someone you know have benefited from the results of scientific research.
Find and discuss a research project that in your opinion has ethical concerns. Explain why you find these concerns to be troubling.
Indicate your personal feelings about the use of animals in research. When should and should not animals be used? What principles have you used to come to these conclusions?

American Psychological Association. (2002). Ethical principles of psychologists. American Psychologist, 57 , 1060–1073.

Baumrind, D. (1985). Research using intentional deception: Ethical issues revisited. American Psychologist, 40 , 165–174.

Kohlberg, L. (1966). A cognitive-developmental analysis of children’s sex-role concepts and attitudes. In E. E. Maccoby (Ed.), The development of sex differences . Stanford, CA: Stanford University Press.

Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper and Row.

Plous, S. (1996). Attitudes toward the use of animals in psychological research and education. Psychological Science, 7 , 352–358.

Popper, K. R. (1959). The logic of scientific discovery . New York, NY: Basic Books.

Rosenthal, R. (1994). Science and ethics in conducting, analyzing, and reporting psychological research. Psychological Science, 5 , 127–134.

Ruble, D., & Martin, C. (1998). Gender development. In W. Damon (Ed.), Handbook of child psychology (5th ed., pp. 933–1016). New York, NY: John Wiley & Sons.

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Thomas, G., & Blackman, D. (1992). The future of animal studies in psychology. American Psychologist, 47 , 1678.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

2.3 Analyzing Findings

Learning objectives.

By the end of this section, you will be able to:

Explain what a correlation coefficient tells us about the relationship between variables
Recognize that correlation does not indicate a cause-and-effect relationship between variables
Discuss our tendency to look for relationships between variables that do not really exist
Explain random sampling and assignment of participants into experimental and control groups
Discuss how experimenter or participant bias could affect the results of an experiment
Identify independent and dependent variables

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Correlational Research

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between variables. The correlation coefficient is usually represented by the letter r .

The number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to 1 (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes. The closer the number is to zero, the weaker the relationship, and the less predictable the relationship between the variables becomes. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. If the variables are not related to one another at all, the correlation coefficient is 0. The example above about ice cream and crime is an example of two variables that we might expect to have no relationship to each other.

The sign—positive or negative—of the correlation coefficient indicates the direction of the relationship ( Figure 2.12 ). A positive correlation means that the variables move in the same direction. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A negative correlation means that the variables move in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa.

The example of ice cream and crime rates is a positive correlation because both variables increase when temperatures are warmer. Other examples of positive correlations are the relationship between an individual’s height and weight or the relationship between a person’s age and number of wrinkles. One might expect a negative correlation to exist between someone’s tiredness during the day and the number of hours they slept the previous night: the amount of sleep decreases as the feelings of tiredness increase. In a real-world example of negative correlation, student researchers at the University of Minnesota found a weak negative correlation ( r = -0.29) between the average number of days per week that students got fewer than 5 hours of sleep and their GPA (Lowry, Dean, & Manders, 2010). Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between hours of sleep and shoe size.

As mentioned earlier, correlations have predictive value. Imagine that you are on the admissions committee of a major university. You are faced with a huge number of applications, but you are able to accommodate only a small percentage of the applicant pool. How might you decide who should be admitted? You might try to correlate your current students’ college GPA with their scores on standardized tests like the SAT or ACT. By observing which correlations were strongest for your current students, you could use this information to predict relative success of those students who have applied for admission into the university.

Link to Learning

Manipulate this interactive scatterplot to practice your understanding of positive and negative correlation.

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, research found that people who eat certain breakfast cereal may have a reduced risk of heart disease (Anderson, Hanna, Peng, & Kryscio, 2000). Cereal companies are likely to share this information in a way that maximizes and perhaps overstates the positive aspects of eating cereal. But does cereal really cause better health, or are there other possible explanations for the health of those who eat cereal? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full ( Figure 2.14 ).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

Causality: Conducting Experiments and Using the Data

As you’ve learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment . Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hair style or a new food. However, in the scientific context, an experiment has precise requirements for design and implementation.

The Experimental Hypothesis

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, the use of technology)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how the use of technology should be limited in the classroom, we have the experimental group learn algebra using a computer program and then test their learning. We measure the learning in our control group after they are taught algebra by a teacher in a traditional classroom. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation.

We also need to precisely define, or operationalize, how we measure learning of algebra. An operational definition is a precise description of our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing learning, we might choose to look at performance on a test covering the material on which the individuals were taught by the teacher or the computer program. We might also ask our participants to summarize the information that was just presented in some way. Whatever we determine, it is important that we operationalize learning in such a way that anyone who hears about our study for the first time knows exactly what we mean by learning. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered use of technology and what is considered learning in our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants spend 45 minutes learning algebra (either through a computer program or with an in-person math teacher) and then give them a test on the material covered during the 45 minutes.

Ideally, the people who score the tests are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how they interpret ambiguous responses, such as sloppy handwriting or minor computational mistakes. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect , you already have some idea as to why this is an important consideration. The placebo effect occurs when people's expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations ( Figure 2.16 ).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how technology use in the classroom affects learning, the independent variable is the type of learning by participants in the study ( Figure 2.17 ). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the learning exhibited by our participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what is the effect of being taught a lesson through a computer program versus through an in-person instructor?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine whom to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves high school students, and we must first generate a sample of students. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment ( Figure 2.18 ). If possible, we should use a random sample (there are other types of samples, but for the purposes of this chapter, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is algebra students. But all algebra students is a very large population, so we need to be more specific; instead we might say our population of interest is all algebra students in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 algebra students who we want to participate in our experiment.

In summary, because we cannot test all of the algebra students in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the algebra students in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Use this online random number generator to learn more about random sampling and assignments.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely it is that any difference found is due to chance (and thus not meaningful). For example, if an experiment is done on the effectiveness of a nutritional supplement, and those taking a placebo pill (and not the supplement) have the same result as those taking the supplement, then the experiment has shown that the nutritional supplement is not effective. Generally, psychologists consider differences to be statistically significant if there is less than a five percent chance of observing them if the groups did not actually differ from one another. Stated another way, psychologists want to limit the chances of making “false positive” claims to five percent or less.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

The Online Writing Lab (OWL) at Purdue University can walk you through the APA writing guidelines.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study's design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve better health without changing their behavior. But if other scientists could not replicate the results, the original study’s claims would be questioned.

In recent years, there has been increasing concern about a “replication crisis” that has affected a number of scientific fields, including psychology. Some of the most well-known studies and scientists have produced research that has failed to be replicated by others (as discussed in Shrout & Rodgers, 2018). In fact, even a famous Nobel Prize-winning scientist has recently retracted a published paper because she had difficulty replicating her results (Nobel Prize-winning scientist Frances Arnold retracts paper, 2020 January 3). These kinds of outcomes have prompted some scientists to begin to work together and more openly, and some would argue that the current “crisis” is actually improving the ways in which science is conducted and in how its results are shared with others (Aschwanden, 2018).

The Vaccine-Autism Myth and Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has indicated that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated ( Figure 2.19 ). Continued reliance on such debunked studies has significant consequences. For instance, between January and October of 2019, there were 22 measles outbreaks across the United States and more than a thousand cases of individuals contracting measles (Patel et al., 2019). This is likely due to the anti-vaccination movements that have risen from the debunked research. For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. There are a number of different types of reliability. Some of these include inter-rater reliability (the degree to which two or more different observers agree on what has been observed), internal consistency (the degree to which different items on a survey that measure the same thing correlate with one another), and test-retest reliability (the degree to which the outcomes of a particular measure remain consistent over multiple administrations).

Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. If the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. While the scale is highly reliable in producing consistent results (e.g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. This is where validity comes into play. Validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure, and once again, there are a number of ways in which validity can be expressed. Ecological validity (the degree to which research results generalize to real-world applications), construct validity (the degree to which a given variable actually captures or measures what it is intended to measure), and face validity (the degree to which a given variable seems valid on the surface) are just a few types that researchers consider. While any valid measure is by necessity reliable, the reverse is not necessarily true. Researchers strive to use instruments that are both highly reliable and valid.

Everyday Connection

How valid are the sat and act.

Standardized tests like the SAT and ACT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT or ACT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT or ACT scores in college admissions is changing based on a number of factors. For one, some researchers assert that these tests are biased, and students from historically marginalized populations are at a disadvantage that unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of these tests is grossly exaggerated in how well they are able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

Recent examples of high profile cheating scandals both domestically and abroad have only increased the scrutiny being placed on these types of tests, and as of March 2019, more than 1000 institutions of higher education have either relaxed or eliminated the requirements for SAT or ACT testing for admissions (Strauss, 2019, March 19).

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Authors: Rose M. Spielman, William J. Jenkins, Marilyn D. Lovett
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Book title: Psychology 2e
Publication date: Apr 22, 2020
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Book URL: https://openstax.org/books/psychology-2e/pages/1-introduction
Section URL: https://openstax.org/books/psychology-2e/pages/2-3-analyzing-findings

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The psychology of experimental psychologists: Overcoming cognitive constraints to improve research: The 47th Sir Frederic Bartlett Lecture

Like many other areas of science, experimental psychology is affected by a “replication crisis” that is causing concern in many fields of research. Approaches to tackling this crisis include better training in statistical methods, greater transparency and openness, and changes to the incentives created by funding agencies, journals, and institutions. Here, I argue that if proposed solutions are to be effective, we also need to take into account human cognitive constraints that can distort all stages of the research process, including design and execution of experiments, analysis of data, and writing up findings for publication. I focus specifically on cognitive schemata in perception and memory, confirmation bias, systematic misunderstanding of statistics, and asymmetry in moral judgements of errors of commission and omission. Finally, I consider methods that may help mitigate the effect of cognitive constraints: better training, including use of simulations to overcome statistical misunderstanding; specific programmes directed at inoculating against cognitive biases; adoption of Registered Reports to encourage more critical reflection in planning studies; and using methods such as triangulation and “pre mortem” evaluation of study design to foster a culture of dialogue and criticism.

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Introduction

The past decade has been a bruising one for experimental psychology. The publication of a paper by Simmons, Nelson, and Simonsohn (2011) entitled “False-positive psychology” drew attention to problems with the way in which research was often conducted in our field, which meant that many results could not be trusted. Simmons et al. focused on “undisclosed flexibility in data collection and analysis,” which is now variously referred to as p -hacking, data dredging, noise mining, or asterisk hunting: exploring datasets with different selections of variables and different analyses to attain a p -value lower than .05 and, subsequently, reporting only the significant findings. Hard on the heels of their demonstration came a wealth of empirical evidence from the Open Science Collaboration (2015) . This showed that less than half the results reported in reputable psychological journals could be replicated in a new experiment.

The points made by Simmons et al. (2011) were not new: indeed, they were anticipated in 1830 by Charles Babbage, who described “cooking” of data:

This is an art of various forms, the object of which is to give ordinary observations the appearance and character of those of the highest degree of accuracy. One of its numerous processes is to make multitudes of observations, and out of these to select only those which agree, or very nearly agree. If a hundred observations are made, the cook must be very unhappy if he cannot pick out fifteen or twenty which will do for serving up. (p. 178–179)

P -hacking refers to biased selection of data or analyses from within an experiment. Bias also affects which studies get published in the form of publication bias—the tendency for positive results to be overrepresented in the published literature. This is problematic because it gives an impression that findings are more consistent than is the case, which means that false theories can attain a state of “canonisation,” where they are widely accepted as true ( Nissen, Magidson, Gross, & Bergstrom, 2016 ). Figure 1 illustrates this with a toy simulation of a set of studies testing a difference between means from two conditions. If we have results from a series of experiments, three of which found a statistically significant difference and three of which did not, this provides fairly strong evidence that the difference is real (panel a). However, if we add a further four experiments that were not reported because results were null, the evidence cumulates in the opposite direction. Thus, omission of null studies can drastically alter our impression of the overall support for a hypothesis.

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The impact of publication bias demonstrated with plots of cumulative log odds in favour of true versus null effect over a series of experiments. The log odds for each experiment can be computed with knowledge of alpha (.05) and power (.8); 1 denotes an experiment with significant difference between means, and 0, a null result. The starting point is zero, indicating that we assume a 50:50 chance of a true effect. For each significant result, the log odds of it coming from a true effect versus a null effect is log(.8/.05) = 2.77. For a null result, the log odds is log (.2/.95) = −1.55. The selected set of studies in panel (a) concludes with a log odds greater than 3, indicating that the likelihood of a true effect is 20 times greater than a null effect. However, panel (b), which includes additional null results (labelled in grey), leads to the opposite conclusion.

Since the paper by Simmons et al. (2011) , there has been a dramatic increase in replication studies. As a result, a number of well-established phenomena in psychology have come into question. Often it is difficult to be certain whether the original reports were false positives, whether the replication was flawed, or whether the effect of interest is only evident under specific conditions—see, for example, Hobson and Bishop (2016) on mu suppression in response to observed actions; Sripada, Kesller, and Jonides (2016) on ego depletion; Lehtonen et al. (2018) on an advantage in cognitive control for bilinguals; O’Donnell et al. (2018) on the professor-priming effect; and Oostenbroek et al. (2016) on neonatal imitation. What is clear is that the size, robustness, and generalisability of many classic effects are lower than previously thought.

Selective reporting, through p -hacking and publication bias, is not the only blight on our science. A related problem is many editors place emphasis on reporting results in a way that “tells a good story,” even if that means retrofitting our hypothesis to the data, i.e., HARKing or “hypothesising after the results are known” ( Kerr, 1998 ). Oberauer and Lewandowsky (2019) drew parallels between HARKing and p -hacking: in HARKing, there is post hoc selection of hypotheses, rather than selection of results or an analytic method. They proposed that HARKing is most widely used in fields where theories are so underspecified that they can accommodate many hypotheses and where there is a lack of “disconfirmatory diagnosticity,” i.e., failure to support a prediction is uninformative.

A lack of statistical power is a further problem for psychology—one that has been recognised since 1969 , when Jacob Cohen exhorted psychologists not to waste time and effort doing experiments that had too few observations to show an effect of interest. In other fields, notably clinical trials and genetics, after a period where non-replicable results proliferated, underpowered studies died out quite rapidly when journals adopted stringent criteria for publication (e.g., Johnston, Lahey, & Matthys, 2013 ), and funders began to require power analysis in grant proposals. Psychology, however, has been slow to catch up.

It is not just experimental psychology that has these problems—studies attempting to link psychological traits and disorders to genetic and/or neurobiological variables are, if anything, subject to greater challenges. A striking example comes from a meta-analysis of links between the serotonin transporter gene, 5-HTTPLR, and depression. This postulated association has attracted huge research interest over the past 20 years, and the meta-analysis included 450 studies. Contrary to expectation, it concluded that there was no evidence of association. In a blog post summarising findings, Alexander (2019) wrote,

. . . what bothers me isn’t just that people said 5-HTTLPR mattered and it didn’t. It’s that we built whole imaginary edifices, whole castles in the air on top of this idea of 5-HTTLPR mattering. We “figured out” how 5-HTTLPR exerted its effects, what parts of the brain it was active in, what sorts of things it interacted with, how its effects were enhanced or suppressed by the effects of other imaginary depression genes. This isn’t just an explorer coming back from the Orient and claiming there are unicorns there. It’s the explorer describing the life cycle of unicorns, what unicorns eat, all the different subspecies of unicorn, which cuts of unicorn meat are tastiest, and a blow-by-blow account of a wrestling match between unicorns and Bigfoot.

It is no exaggeration to say that our field is at a crossroads ( Pashler & Wagenmakers, 2012 ), and the 5-HTTLPR story is just a warning sign that practices that lead to bad science are widespread. If we continue to take the well-trodden path, using traditional methods for cooking data and asterisk hunting, we are in danger of losing attention, respect, and funding.

Much has been written about how we might tackle the so-called “replication crisis.” There have been four lines of attack. First, there have been calls for greater openness and transparency ( Nosek et al., 2015 ). Second, a case has been made for better training in methods (e.g., Rousselet, Pernet, & Wilcox, 2017 ). Third, it has been argued we need to change the way research has been conducted to incorporate pre-registration of research protocols, preferably in the format of Registered Reports, which are peer-reviewed prior to data collection ( Chambers, 2019 ). Fourth, it is recognised that for too long, the incentive structure of research has prioritised innovative, groundbreaking results over methodological quality. Indeed, Smaldino and McElreath (2016) suggested that one can model the success of scientists in a field as an evolutionary process, where prestigious publications lead to survival, leaving those whose work is less exciting to wither away and leave science. The common thread to these efforts is that they locate the mechanisms of bad science at the systemic level, in ways in which cultures and institutions reinforce norms and distribute resources. The solutions are, therefore, aimed at correcting these shortcomings by creating systems that make good behaviour easier and more rewarding and make poor behaviour more costly.

My view, however, is that institutional shortcomings are only part of the story: to improve scientific research, we also need to understand the mechanisms that maintain bad practices in individual humans. Bad science is usually done because somebody mistook it for good science. Understanding why individual scientists mistake bad science for good, and helping them to resist these errors, is a necessary component of the movement to improve psychology. I will argue that we need to understand how cognitive constraints lead to faulty reasoning if we are to get science back on course and persuade those who set the incentives to reform. Fortunately, as psychologists, we are uniquely well positioned to tackle this issue.

Experimental psychology has a rich tradition of studying human reasoning and decision-making, documenting the flaws and foibles that lead us to selectively process some types of information, make judgements on the basis of incomplete evidence, and sometimes behave in ways that seem frankly irrational. This line of work has had significant application to economics, politics, business studies, and law, but, with some notable exceptions (e.g., Hossenfelder, 2018 ; Mahoney, 1976 ), it has seldom been considered when studying the behaviour of research scientists. In what follows, I consider how our knowledge of human cognition can make sense of problematic scientific practices, and I propose ways we might use this information to find solutions.

Cognitive constraints that affect how psychological science is done

Table 1 lists four characteristics of human cognition that I focus on: I refer to these as “constraints” because they limit how we process, understand, or remember information, but it is important to note that they include some biases that can be beneficial in many contexts. The first constraint is confirmation bias. As Hahn and Harris (2014) noted, a range of definitions of “confirmation bias” exist—here, I will define it as the tendency to seek out evidence that supports our position. A further set of constraints has to do with understanding of probability. A lack of an intuitive grasp of probability contributes to both neglect of statistical power in study design and p -hacking in data analysis. Third, there is an asymmetry in moral reasoning that can lead us to treat errors of omission as less culpable than errors of commission, even when their consequences are equally serious ( Haidt & Baron, 1996 ). The final constraint featured in Bartlett’s (1932) work: reliance on cognitive schemata to fill in unstated information, leading to “reconstructive remembering,” which imbues memories with meaning while filtering out details that do not fit preconceptions.

Different types of cognitive constraints.

In what follows, I illustrate how these constraints assume particular importance at different stages of the research process, as shown in Table 2 .

Cognitive constraints that operate at different stages of the research process.

HARKing: hypothesising after the results are known.

Bias in experimental design

Confirmation bias and the failure to consider alternative explanations.

Scientific discovery involves several phases: the researcher needs to (a) assemble evidence, (b) look for meaningful patterns and regularities in the data, (c) formulate a hypothesis, and (d) test it empirically by gathering informative new data. Steps (a)–(c) may be designated as exploratory and step (d) as hypothesis testing or confirmatory ( Wagenmakers, Wetzels, Borsboom, van der Mass, & Kievit, 2012 ). Importantly, the same experiment cannot be used to both formulate and confirm a hypothesis. In practice, however, the distinction between the two types of experiment is often blurred.

Our ability to see patterns in data is vital at the exploratory stage of research: indeed, seeing something that nobody else has observed is a pinnacle of scientific achievement. Nevertheless, new ideas are often slow to be accepted, precisely because they do not fit the views of the time. One such example is described by Zilles and Amunts (2010) : Brodmann’s cytoarchitectonic map of the brain, described in 1909. This has stood the test of time and is still used over 100 years later, but for several decades, it was questioned by those who could not see the fine distinctions made by Brodmann. Indeed, criticisms of poor reproducibility and lack of objectivity were levelled against him.

Brodmann’s case illustrates that we need to be cautious about dismissing findings that depend on special expertise or unique insight of the observer. However, there are plenty of other instances in the history of science where invalid ideas persisted, especially if proposed by an influential or charismatic figure. Entire edifices of pseudoscience have endured because we are very bad at discarding theories that do not work; as Bartlett (1932) would predict, new information that is consistent with the theory will strengthen its representation in our minds, but inconsistent information will be ignored. Examples from the history of science include the rete mirabile , a mass of intertwined arteries that is found in sheep but wrongly included in anatomical drawings of humans for over 1,000 years because of the significance attributed to this structure by Galen ( Bataille et al., 2007 ); the planet Vulcan, predicted by Newton’s laws and seen by many astronomers until its existence was disproved by Einstein’s discoveries ( Levenson, 2015 ); and N-rays, non-existent rays seen by at least 40 people and analysed in 3,090 papers by 100 scientists between 1903 and 1906 ( Nye, 1980 ).

Popper’s (1934/ 1959 ) goal was to find ways to distinguish science from pseudoscience, and his contribution to philosophy of science was to emphasise that we should be bold in developing ideas but ruthless in attempts to falsify them. In an early attempt to test scientists’ grasp of Popperian logic, Mahoney (1976) administered a classic task developed by Wason (1960) to 84 scientists (physicists, biologists, psychologists, and sociologists). In this deceptively simple task, people are shown four cards and told that each card has a number on one side and a patch of colour on the other side. The cards are placed to show number 3, number 8, red, and blue, respectively (see Figure 2 ). The task is to identify which cards need to be turned over to test the hypothesis that if an even number appears on one side, then the opposite side is red. The subject can pick any number of cards. The correct response is to name the two cards that could disconfirm the hypothesis—the number 8 and the blue card. Fewer than 10% of the scientists tested by Mahoney identified both critical cards, more often selecting the number 8 and the red card.

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Wason’s (1960) task: The subject is told, “Each card has a number on one side and a patch of colour on the other. You are asked to test the hypothesis that—for these 4 cards—if an even number appears on one side, then the opposite side is red. Which card(s) would you turn over to test the hypothesis?”

Although this study was taken as evidence of unscientific reasoning by scientists, that conclusion has since been challenged by those who have criticised both Popperian logic, in general, and the Wason selection task, in particular, as providing an unrealistic test of human rationality. For a start, the Wason task uses a deterministic hypothesis that can be disproved by a single piece of evidence. This is not a realistic model of biological or behavioural sciences, where we seldom deal with deterministic phenomena. Consider the claim that smoking causes lung cancer. Most of us accept that this is so, even though we know there are people who smoke and who do not get lung cancer and people who get lung cancer but never smoked. When dealing with probabilistic phenomena, a Bayesian approach makes more sense, whereby we consider the accumulated evidence to determine the relative likelihood of one hypothesis over another (as illustrated in Figure 1 ). Theories are judged as more or less probable, rather than true or false. Oaksford and Chater (1994) showed that, from a Bayesian perspective, typical selections made on the Wason task would be rational in contexts where the antecedent and consequent of the hypothesis (an even number and red colour) were both rare. Subsequently, Perfors and Navarro (2009) concluded that in situations where rules are relevant only for a minority of entities, then confirmation bias is an efficient strategy.

This kind of analysis has shifted the focus to discussions about how far, and under what circumstances, people are rational decision-makers. However, it misses a key point about scientific reasoning, which is that it involves an active process of deciding which evidence to gather, rather than merely a passive evaluation of existing evidence. It seems reasonable to conclude that, when presented with a particular set of evidence, people generally make decisions that are rational when evaluated against Bayesian standards. However, history suggests that we are less good at identifying which new evidence needs to be gathered to evaluate a theory. In particular, people appear to have a tendency to accept a hypothesis on the basis of “good enough” evidence, rather than actively seeking evidence for alternative explanations. Indeed, an early study by Doherty, Mynatt, Tweney, and Schiavo (1979) found that, when given an opportunity to select evidence to help decide which of two hypotheses was true (in a task where a fictitious pot had to be assigned as originating from one of the two islands that differed in characteristic features), people seemed unable to identify which information would be diagnostic and tended, instead, to select information that could neither confirm nor disconfirm their hypothesis.

Perhaps the strongest evidence for our poor ability to consider alternative explanations comes from the history of the development of clinical trials. Although James Lind is credited with doing the first trials for treatment of scurvy in 1747, it was only in 1948 that the randomised controlled trial became the gold standard for evaluating medical interventions ( Vallier & Timmerman, 2008 ). The need for controls is not obvious, and people who are not trained in this methodology will often judge whether a treatment is effective on the basis of a comparison on an outcome measure between a pre-treatment baseline and a post-treatment evaluation. The logic is that if a group of patients given the treatment does not improve, the treatment did not work. If they do show meaningful gains, then it did work. And we can even embellish this comparison with a test of statistical significance. This reasoning can be seen as entirely rational, and this can explain why so many people are willing to accept that alternative medicine is effective.

The problem with this approach is that the pre–post intervention comparison allows important confounds to creep in. For instance, early years practitioners argue that we should identify language problems in toddlers so that we can intervene early. They find that if 18-month-old late talkers are given intervention, only a minority still have language problems at 2 years and, therefore, conclude the intervention was effective. However, if an untreated control group is studied over the same period, we find very similar rates of improvement ( Wake et al., 2011 )—presumably due to factors such a spontaneous resolution of problems or regression to the mean, which will lead to systematic bias in outcomes. Researchers need training to recognise causes of bias and to take steps to overcome them: thinking about possible alternative explanations of an observed phenomenon does not come naturally, especially when the preliminary evidence looks strong.

Intervention studies provide the clearest evidence of what I term “premature entrenchment” of a theory: some other examples are summarised in Table 3 . Note that these examples do not involve poor replicability, quite the opposite. They are all cases where an effect, typically an association between variables, is reliably observed, and researchers then converge on accepting the most obvious causal explanation, without considering lines of evidence that might point to alternative possibilities.

Premature entrenchment: examples where the most obvious explanation for an observed association is accepted for many years, without considering alternative explanations that could be tested with different evidence.

fMRI: functional magnetic resonance imaging.

Premature entrenchment may be regarded as evidence that humans adopt Bayesian reasoning: we form a prior belief about what is the case and then require considerably more evidence to overturn that belief than to support it. This would explain why, when presented with virtually identical studies that either provided support for or evidence against astrology, psychologists were more critical of the latter ( Goodstein & Brazis, 1970 ). The authors of that study expressed concern about the “double standard” shown by biased psychologists who made unusually harsh demands of research in borderline areas, but from a Bayesian perspective, it is reasonable to use prior knowledge so that extraordinary claims require extraordinary evidence. Bayesian reasoning is useful in many situations: it allows us to act decisively on the basis of our long-term experience, rather than being swayed by each new incoming piece of data. However, it can be disastrous if we converge on a solution too readily on the basis of incomplete or inaccurate information. This will be exacerbated by publication bias, which distorts the evidential landscape.

For many years, the only methods available to counteract the tendency for premature entrenchment were exhortations to be self-critical (e.g., Feynman, 1974 ) and peer review. The problem with peer review is that it typically comes too late to be useful, after research is completed. In the final section of this article, I will consider some alternative approaches that bring in external appraisal of experimental designs at an earlier stage in the research process.

Misunderstanding of probability leading to underpowered studies

Some 17 years after Cohen’s seminal work on statistical power, Newcombe (1987) wrote,

Small studies continue to be carried out with little more than a blind hope of showing the desired effect. Nevertheless, papers based on such work are submitted for publication, especially if the results turn out to be statistically significant. (p. 657)

In clinical medicine, things have changed, and the importance of adequate statistical power is widely recognised among those conducting clinical trials. But in psychology, the “blind hope” has persisted, and we have to ask ourselves why this is.

My evidence here is anecdotal, but the impression is that many psychologists simply do not believe advice about statistical power, perhaps because there are so many underpowered studies published in the literature. When a statistician is consulted about sample size for a study, he or she will ask the researcher to estimate the anticipated effect size. This usually leads to a sample size estimate that is far higher than the researcher anticipated or finds feasible, leading to a series of responses not unlike the first four of the five stages of grief: denial, anger, bargaining, and depression. The final stage, acceptance, may, however, not be reached.

Of course, there are situations where small sample sizes are perfectly adequate: the key issue is how large the effect of interest is in relation to the variance. In some fields, such as psychophysics, you may not even need statistics—the famous “interocular trauma” test (referring to a result so obvious and clear-cut that it hits you between the eyes) may suffice. Indeed, in such cases, recruitment of a large sample would just be wasteful.

There are, however, numerous instances in psychology where people have habitually used sample sizes that are too small to reliably detect an effect of interest: see, for instance, the analysis by Poldrack et al. (2017) of well-known effects in functional magnetic resonance imaging (fMRI) or Oakes (2017) on looking-time experiments in infants. Quite often, a line of research is started when a large effect is seen in a small sample, but over time, it becomes clear that this is a case of “winner’s curse,” a false positive that is published precisely because it looks impressive but then fails to replicate when much larger sample sizes are used. There are some recent examples from studies looking at neurobiological or genetic correlates of individual differences, where large-scale studies have failed to support previously published associations that had appeared to be solid (e.g., De Kovel & Francks, 2019 , on genetics of handedness; Traut et al., 2018 , on cerebellar volume in autism; Uddén et al., 2019 , on genetic correlates of fMRI language-based activation).

A clue to the persistence of underpowered psychology studies comes from early work by Tversky and Kahneman (1971 , 1974 ). They studied a phenomenon that they termed “belief in the law of small numbers,” an exaggerated confidence in the validity of conclusions based on small samples, and showed that even those with science training tended to have strong intuitions about random sampling that were simply wrong. They illustrated this with the following problem:

A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more such days? 1. The large hospital 2. The small hospital 3. About the same (that is, within 5% of each other)

Most people selected Option 3, whereas, as illustrated in Figure 3 , Option 2 is the correct answer—with only 15 births per day, the day-to-day variation in the proportion of boys will be much higher than with 45 births per day, and hence, more days will have more than 60% boys. One reason why our intuitions deceive us is because the sample size does not affect the average percentage of male births in the long run: this will be 50%, regardless of the hospital size. But sample size has a dramatic impact on the variability in the proportion of male births from day to day. More formally, if you have a big and small sample drawn from the same population, the expected estimate of the mean will be the same, but the standard error of that estimate will be greater for the small sample.

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Simulated data showing proportions of males born in a small hospital with 15 births per day versus a large hospital with 45 births per day. The small hospital has more days where more than 60% of births are boys (points above red line).

Statistical power depends on the effect size, which, for a simple comparison of two means, can be computed as the difference in means divided by the pooled standard deviation. It follows that power is crucially dependent on the proportion of variance in observations that is associated with an effect of interest, relative to background noise. Where variance is high, it is much harder to detect the effect, and hence, small samples are often underpowered. Increasing the sample size is not the only way to improve power: other options include improving the precision of measurement, using more effective manipulations, or adopting statistical approaches to control noise ( Lazic, 2018 ). But in many situations, increasing the sample size is the preferred approach to enhance statistical power to detect an effect.

Bias in data analysis: p -hacking

P -hacking can take various forms, but they all involve a process of selective analysis. Suppose some researchers hypothesise that there is an association between executive function and implicit learning in a serial reaction time task, and they test this in a study using four measures of executive function. Even if there is only one established way of scoring each task, they have four correlations; this means that the probability that none of the correlations is significant at the .05 level is .95 4 —i.e., .815—and conversely, the probability that at least one is significant is .185. This probability can be massaged to even higher levels if the experimenters look at the data and then select an analytic approach that maximises the association: maybe by dropping outliers, by creating a new scoring method, combining measures in composites, and so on. Alternatively, the experimenters may notice that the strength of the correlation varies with the age or sex of participants and so subdivide the sample to coax at least a subset of data into significance. The key thing about p -hacking is that at the end of the process, the researchers selectively report the result that “worked,” with the implication that the p -value can be interpreted at face value. But it cannot: probability is meaningless if not defined in terms of a particular analytic context. P -hacking appears to be common in psychology ( John, Loewenstein, & Prelec, 2012 ). I argue here that this is because it arises from a conjunction of two cognitive constraints: failure to understand probability, coupled with a view that omission of information when reporting results is not a serious misdemeanour.

Failure to understand probability

In an influential career guide, published by the American Psychological Association, Bem (2004) explicitly recommended going against the “conventional view” of the research process, as this might lead us to miss exciting new findings. Instead readers were encouraged to

become intimately familiar with . . . the data. Examine them from every angle. Analyze the sexes separately. Make up new composite indexes. If a datum suggests a new hypothesis, try to find additional evidence for it elsewhere in the data. If you see dim traces of interesting patterns, try to reorganize the data to bring them into bolder relief. If there are participants you don’t like, or trials, observers, or interviewers who gave you anomalous results, drop them (temporarily). Go on a fishing expedition for something—anything—interesting. (p. 2)

For those who were concerned this might be inappropriate, Bem offered reassurance. Everything is fine because what you are doing is exploring your data. Indeed, he implied that anyone who follows the “conventional view” would be destined to do boring research that nobody will want to publish.

Of course, Bem (2004) was correct to say that we need exploratory research. The problem comes when exploratory research is repackaged as if it were hypothesis testing, with the hypothesis invented after observing the data (HARKing), and the paper embellished with p -values that are bound to be misleading because they were p -hacked from numerous possible values, rather than derived from testing an a priori hypothesis. If results from exploratory studies were routinely replicated, prior to publication, we would not have a problem, but they are not. So why did the American Psychological Association think it appropriate to publish Bem’s views as advice to young researchers? We can find some clues in the book overview, which explains that there is a distinction between the “formal” rules that students are taught and the “implicit” rules that are applied in everyday life, concluding that “This book provides invaluable guidance that will help new academics plan, play, and ultimately win the academic career game.” Note that the stated goal is not to do excellent research: it is to have “a lasting and vibrant career.” It seems, then, that there is recognition here that if you do things in the “conventional” way, your career will suffer. It is clear from Bem’s framing of his argument that he was aware that his advice was not “conventional,” but he did not think it was unethical—indeed, he implied it would be unfair on young researchers to do things conventionally as that will prevent them making exciting discoveries that will enable them to get published and rise up the academic hierarchy. While it is tempting to lament the corruption of a system that treats an academic career as a game, it is more important to consider why so many people genuinely believe that p -hacking is a valid, and indeed creative, approach to doing research.

The use of null-hypothesis significance testing has attracted a lot of criticism, with repeated suggestions over the years that p -values be banned. I favour the more nuanced view expressed by Lakens (2019) , who suggests that p -values have a place in science, provided they are correctly understood and used to address specific questions. There is no doubt, however, that many people do misunderstand the p -value. There are many varieties of misunderstanding, but perhaps the most common is to interpret the p -value as a measure of strength of evidence that can be attached to a given result, regardless of the context. It is easy to see how this misunderstanding arises: if we hold the sample size constant, then for a single comparison, there will be a linear relationship between the p -value and the effect size. However, whereas an effect size remains the same, regardless of the analytic context, a p -value is crucially context-dependent.

Suppose in the fictitious study of executive function described above, the researchers have 20 participants and four measures of executive function (A–D) that correlate with implicit learning with r values of .21, .47, .07, and −.01. The statistics package tells us that the corresponding two-tailed p -values are .374, .037, .769, and .966. A naive researcher may rejoice at having achieved significance with the second correlation. However, as noted above, the probability that at least one correlation of the four will have an associated p -value of less than .05 is 18%, not 5%. If we want to identify correlations that are unlikely under the null hypothesis, then we need to correct the alpha level (e.g., by doing a Bonferroni correction to adjust by the number of tests, i.e., .05/4 = .0125). At this point, the researchers see their significant result snatched from their grasp. This creates a strong temptation to just drop the three non-significant tests and not report them. Alternatively, one sometimes sees papers that report the original p -value but then state that it “did not survive” Bonferroni correction, but they, nevertheless, exhume it and interpret the uncorrected value. Researchers acting this way may not think that they are doing anything inappropriate, other than going against advice of pedantic statisticians, especially given Bem’s (2004) advice to follow the “implicit” rather than “formal” rules of research. However, this is simply wrong: as illustrated above, a p -value can only be interpreted in relation to the context in which it is computed.

One way of explaining the notion of p -hacking is to use the old-fashioned method of games of chance. I find this scenario helpful: we have a magician who claims he can use supernatural powers to deal a poker hand of “three of a kind” from an unbiased deck of cards. This type of hand will occur in around 1 of 50 draws from an unbiased deck. He points you to a man who, to his amazement, finds that his hand contains three of a kind. However, you then discover he actually tried his stunt with 50 people, and this man was the only one who got three of a kind. You are rightly disgruntled. This is analogous to p -hacking. The three-of-a-kind hand is real enough, but its unusualness, and hence its value as evidence of the supernatural, depends on the context of how many tests were done. The probability that needs to be computed here is not the probability of one specific result but rather the probability that specific result would come up at least once in 50 trials.

Asymmetry of sins of omission and commission

According to Greenwald (1975) “[I]t is a truly gross ethical violation for a researcher to suppress reporting of difficult-to-explain or embarrassing data to present a neat and attractive package to a journal editor” (p. 19).

However, this view is not universal.

Greenwald’s focus was on publication bias, i.e., failure to report an entire study, but the point he made about “prejudice” against null results also applies to cases of p -hacking where only “significant” results are reported, whereas others go unmentioned. It is easy to see why scientists might play down the inappropriateness of p -hacking, when it is so important to generate “significant” findings in a world with a strong prejudice against null results. But I suspect another reason why people tend to underrate the seriousness of p -hacking is because it involves an error of omission (failing to report the full context of a p -value), rather than an error of commission (making up data).

In studies of morality judgement, errors of omission are generally regarded as less culpable than errors of commission (see, e.g., Haidt & Baron, 1996 ). Furthermore, p -hacking may be seen to involve a particularly subtle kind of dishonesty because the statistics and their associated p -values are provided by the output of statistics software. They are mathematically correct when testing a specific, prespecified hypothesis: the problem is that, without the appropriate context, they imply stronger evidence than is justified. This is akin to what Rogers, Zeckhauser, Gino, Norton, and Schweitzer (2017) have termed “paltering,” i.e., the use of truthful statements to mislead, a topic they studied in the context of negotiations. An example was given of a person trying to sell a car that had twice needed a mechanic to fix it. Suppose the potential purchaser directly asks “Has the car ever had problems?” An error of commission is to deny the problems, but a paltering answer would be “This car drives very smoothly and is very responsive. Just last week it started up with no problems when the temperature was −5 degrees Fahrenheit.” Rogers et al. showed that negotiators were more willing to palter than to lie, although potential purchasers regarded paltering as only marginally less immoral than lying.

Regardless of the habitual behaviour of researchers, the general public does not find p -hacking acceptable. Pickett and Roche (2018) did an M-Turk experiment in which a community sample was asked to judge the morality of various scenarios, including this one:

A medical researcher is writing an article testing a new drug for high blood pressure. When she analyzes the data with either method A or B, the drug has zero effect on blood pressure, but when she uses method C, the drug seems to reduce blood pressure. She only reports the results of method C, which are the results that she wants to see.

Seventy-one percent of respondents thought this behaviour was immoral, 73% thought the researcher should receive a funding ban, and 63% thought the researcher should be fired.

Nevertheless, although selective reporting was generally deemed immoral, data fabrication was judged more harshly, confirming that in this context, as in those studied by Haidt and Baron (1996) , sins of commission are taken more seriously than errors of omission.

If we look at the consequences of a specific act of p -hacking, it can potentially be more serious than an act of data fabrication: this is most obvious in medical contexts, where suppression of trial results, either by omitting findings from within a study or by failing to publish studies with null results, can provide a badly distorted basis for clinical decision-making. In their simulations of evidence cumulation, Nissen et al. (2016) showed how p -hacking could compound the impact of publication bias and accelerate the premature “canonization” of theories; the alpha level that researchers assume applies to experimental results is distorted by p -hacking, and the expected rate of false positives is actually much higher. Furthermore, p -hacking is virtually undetectable because the data that are presented are real, but the necessary context for their interpretation is missing. This makes it harder to correct the scientific record.

Bias in writing up a study

Most writing on the “replication crisis” focuses on aspects of experimental design and observations, data analysis, and scientific reporting. The resumé of literature that is found in the introduction to empirical papers, as well as in literature review articles, is given less scrutiny. I will make the case that biased literature reviews are universal and have a major role in sustaining poor reproducibility because they lead to entrenchment of false theories, which are then used as the basis for further research.

It is common to see biased literature reviews that put a disproportionate focus on findings that are consistent with the author’s position. Researchers who know an area well may be aware of this, especially if their own work is omitted, but in general, cherry-picking of evidence is hard to detect. I will use a specific paper published in 2013 to illustrate my point, but I will not name the authors, as it would be invidious to single them out when the kinds of bias in their literature review are ubiquitous. In their paper, my attention was drawn to the following statement in the introduction:

Regardless of etiology, cerebellar neuropathology commonly occurs in autistic individuals. Cerebellar hypoplasia and reduced cerebellar Purkinje cell numbers are the most consistent neuropathologies linked to autism. … MRI studies report that autistic children have smaller cerebellar vermal volume in comparison to typically developing children.

I was surprised to read this because a few years ago, I had attended a meeting on neuroanatomical studies of autism and had come away with the impression that there were few consistent findings. I did a quick search for an up-to-date review, which turned up a meta-analysis ( Traut et al., 2018 ), that included 16 MRI studies published between 1997 and 2010, five of which reported larger cerebellar size in autism and one of which found smaller cerebellar size. In the article I was reading, one paper had been cited to support the MRI statement, but it referred to a study where the absolute size of the vermis did not differ from typically developing children but was relatively small in the autistic participants, after the overall (larger) size of the cerebellum had been controlled for.

Other papers cited to support the claims of cerebellar neuropathology included a couple of early post mortem neuroanatomical studies, as well as two reviews. The first of these ( DiCicco-Bloom et al., 2006 ) summarised presentations from a conference and supported the claims made by the authors. The other one, however ( Palmen, van Engeland, Hof, & Schmitz, 2004 ), expressed more uncertainty and noted a lack of correspondence between early neuroanatomical studies and subsequent MRI findings, concluding,

Although some consistent results emerge, the majority of the neuropathological data remain equivocal. This may be due to lack of statistical power, resulting from small sample sizes and from the heterogeneity of the disorder itself, to the inability to control for potential confounding variables such as gender, mental retardation, epilepsy and medication status, and, importantly, to the lack of consistent design in histopathological quantitative studies of autism published to date.

In sum, a confident statement “cerebellar neuropathology commonly occurs in autistic individuals,” accompanied by a set of references, converged to give the impression that there is consensus that the cerebellum is involved in autism. However, when we drill down, we find that the evidence is uncertain, with discrepancies between neuropathological studies and MRI and methodological concerns about the former. Meanwhile, this study forms part of a large body of research in which genetically modified mice with cerebellar dysfunction are used as an animal model of autism. My impression is that few of the researchers using these mouse models appreciate that the claim of cerebellar abnormality in autism is controversial among those working with humans because each paper builds on the prior literature. There is entrenchment of error, for two reasons. First, many researchers will take at face value the summary of previous work in a peer-reviewed paper, without going back to original cited sources. Second, even if a researcher is careful and scholarly and does read the cited work, they are unlikely to find relevant studies that were not included in the literature review.

It is easy to take an example like this and bemoan the lack of rigour in scientific writing, but this is to discount cognitive biases that make it inevitable that, unless we adopt specific safeguards against this, cherry-picking of evidence will be the norm. Three biases lead us in this direction: confirmation bias, moral asymmetry, and reliance on schemata.

Confirmation bias: cherry-picking prior literature

A personal example may serve to illustrate the way confirmation bias can operate subconsciously. I am interested in genetic effects on children’s language problems, and I was in the habit of citing three relevant twin studies when I gave talks on this topic. All these obtained similar results, namely that there was a strong genetic component to developmental language disorders, as evidenced by much higher concordance for disorder in pairs of monozygotic versus dizygotic twins. In 2005 , however, Hayiou-Thomas, Oliver, and Plomin published a twin study with very different findings, with low twin/co-twin concordance, regardless of zygosity. It was only when I came to write a review of this area and I checked the literature that I realised I had failed to mention the 2005 study in talks for a year or two, even though I had collaborated with the authors and was well aware of the findings. I had formed a clear view on heritability of language disorders, and so I had difficulty remembering results that did not agree. Subsequently, I realised we should try to understand why this study obtained different results and found a plausible explanation ( Bishop & Hayiou-Thomas, 2008 ). But I only went back for a further critical look at the study because I needed to make sense of the conflicting results. It is inevitable that we behave this way as we try to find generalisable results from a body of work, but it creates an asymmetry of attention and focus between work that we readily accept, because it fits, and work that is either forgotten or looked at more critically, because it does not.

A particularly rich analysis of citation bias comes from a case study by Greenberg (2009) , who took as his starting point papers concerned with claims that a protein, β amyloid, was involved in causing a specific form of muscle disease. Greenberg classified papers according to whether they were positive, negative, or neutral about this claim and carried out a network analysis to identify influential papers (those with many citations). He found that papers that were critical of the claim received far fewer citations than those that supported it, and this was not explained by lower quality. Animal model studies were almost exclusively justified by selective citation of positive studies. Consistent with the idea of “reconstructive remembering,” he also found instances where cited content was distorted, as well as cases where influential review papers amplified citation bias by focusing attention only on positive work. The net result was an information (perhaps better termed a disinformation) cascade that would lead to a lack of awareness of critical data, which never gets recognised. In effect, when we have agents that adopt Bayesian reasoning, if they are presented with distorted information, this creates a positive feedback loop that leads to increasing bias in the prior. Viewed this way, we can start to see how omission of relevant citations is not a minor peccadillo but a serious contributor to entrenchment of error. Further evidence of the cumulative impact of citation bias is shown in Figure 4 , which uses studies of intervention for depression. Because studies in this area are registered, it is possible to track the fate of unpublished as well as published studies. The researchers showed that studies with null results are far less likely to be published than those with positive findings, but even if the former are published, there is a bias against citing them.

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The cumulative impact of reporting and citation biases on the evidence base for antidepressants. (a) Displays the initial, complete cohort of trials that were recorded in a registry, while (b) through (e) show the cumulative effect of biases. Each circle indicates a trial, while the colour indicates whether results were positive or negative or were reported to give a misleadingly positive impression(spin). Circles connected by a grey line indicate trials from the same publication. The progression from (a) to (b) shows that nearly all the positive trials but only half of those with null results were published, and reporting of null studies showed (c) bias or (d) spin in what was reported. In (e), the size of the circle indicates the (relative) number of citations received by that category of studies.

Source. Reprinted with permission from De Vries et al. (2018) .

While describing such cases of citation bias, it is worth pausing to consider one of the best-known examples of distorted thinking: experimenter bias. This is similar to confirmation bias, but rather than involving selective attention to specific aspects of a situation that fits with our preconceptions, it has a more active character, whereby the experimenter can unwittingly influence the outcome of a study. The best-known research on this topic was the original Rosenthal and Fode (1963) study, where students were informed that the rats they were studying were “maze-bright” or “maze-dull,” when in fact they did not differ. Nevertheless, the “maze-bright” group learned better, suggesting that the experimenter would try harder to train an animal thought to have potential. A related study by Rosenthal and Jacobson (1963) claimed that if teachers were told that a test had revealed that specific pupils were “ready to bloom,” they would do better on an IQ test administered at the end of the year, even though the children so designated were selected at random.

Both these studies are widely cited. It is less well known that work on experimenter bias was subjected to a scathing critique by Barber and Silver (1968) , entitled “Fact, fiction and the experimenter bias effect,” in which it was noted that work in this area suffered from poor methodological quality, in particular p -hacking. Barber and Silver did not deny that experimenter bias could affect results, but they concluded that these effects were far less common and smaller in magnitude than those implied by Rosenthal’s early work. Subsequently, Barber (1976) developed this critique further in his book Pitfalls in Human Research. Yet Rosenthal’s work is more highly cited and better remembered than that of Barber.

Rosenthal’s work provides a cautionary tale: although confirmation bias helps explain distorted patterns of citation, the evidence for maladaptive cognitive biases has been exaggerated. Furthermore, studies on confirmation bias often use artificial experiments, divorced from real life, and the criteria for deciding that reasoning is erroneous are often poorly justified ( Hahn & Harris, 2014 ). In future, it would be worthwhile doing more naturalistic explorations of people’s memory for studies that do and do not support a position when summarising scientific literature.

On a related point, in using confirmation bias as an explanation for persistence of weak theories, there is a danger that I am falling into exactly the trap that I am describing. For instance, I was delighted to find Greenberg’s (2009) paper, as it chimed very well with my experiences when reading papers about cerebellar deficits in autism. But would I have described and cited it here if it had shown no difference between citations for papers that did and did not support the β amyloid claim? Almost certainly not. Am I going to read all literature on citation bias to find out how common it is? That strategy would soon become impossible if I tried to do it for every idea I touch upon in this article.

Moral asymmetry between errors of omission and commission

The second bias that fortifies the distortions in a literature review is the asymmetry of moral judgement that I referred to when discussing p -hacking. To my knowledge, paltering has not been studied in the context of literature reviews, but my impression is that selective presentation of results that fit, while failing to mention important contextual factors (e.g., the vermis in those with autism is smaller but only when you have covaried for the total cerebellar size), is common. How far this is deliberate or due to reconstructive remembering, however, is impossible to establish.

It would also be of interest to conduct studies on people’s attitudes to the acceptability of cherry-picking of literature versus paltering (misleadingly selective reporting) or invention of a study. I would anticipate that most would regard cherry-picking as fairly innocuous, for several reasons: first, it could be an unintended omission; second, the consequences of omitting material from a review may be seen as less severe than introducing misinformation; and third, selective citation of papers that fit a narrative can have a positive benefit in terms of readability. There are also pragmatic concerns: some journals limit the word count for an introduction or reference list so that full citation of all relevant work is not possible and, finally, sanctioning people for harmful omissions would create apparently unlimited obligations ( Haidt & Baron, 1996 ). Quite simply, there is far too much literature for even the most diligent scholar to read.

Nevertheless, consequences of omission can be severe. The above examples of research on the serotonin transporter gene in depression, or cerebellar abnormality in autism, emphasise how failure to cite earlier null results can lead to a misplaced sense of confidence in a phenomenon, which is wasteful in time and money when others attempt to build on it. And the more we encounter a claim, the more likely it is to be judged as true, regardless of actual accuracy (see Pennycook, Cannon, & Rand, 2018 , for a topical example). As Ingelfinger (1976) put it, “faulty or inappropriate references . . . like weeds, tend to reproduce themselves and so permit even the weakest of allegations to acquire, with repeated citation, the guise of factuality” (p. 1076).

Reliance on schemata

Our brains cannot conceivably process all the information around us: we have to find a way to select what is important to function and survive. This involves a search for meaningful patterns, which once established, allow us to focus on what is relevant and ignore the rest. Scientific discovery may be seen as an elevated version of pattern discovery: we see the height of scientific achievement as discovering regularities in nature that allow us to make better predictions about how the world behaves and to create new technologies and interventions from the basic principles we have discovered.

Scientific progress is not a simple process of weighing up competing pieces of evidence in relation to a theory. Rather than simply choosing between one hypothesis and another, we try to understand a problem in terms of a schema. Bartlett (1932) was one of the first psychologists to study how our preconceptions, or schemata, create distortions in perception and memory. He introduced the idea of “reconstructive remembering,” demonstrating how people’s memory of a narrative changed over time in specific ways, to become more coherent and aligned with pre-existing schemata.

Bartlett’s (1932) work on reconstructive remembering can explain why we not only tend to ignore inconsistent evidence ( Duyx, Urlings, Swaen, Bouter, & Zeegers, 2017 ) but also are prone to distort the evidence that we do include ( Vicente & Brewer, 1993 ). If we put together the combined influence of confirmation bias and reconstructive remembering, it suggests that narrative literature reviews have a high probability of being inaccurate: both types of bias will lead to a picture of research converging on a compelling story, when the reality may be far less tidy ( Katz, 2013 ).

I have focused so far on bias in citing prior literature, but schemata also influence how researchers go about writing up results. If we just were to present a set of facts that did not cohere, our work would be difficult to understand and remember. As Chalmers, Hedges, and Cooper (2002) noted, this point was made in 1885 by Lord Raleigh in a presidential address to the British Association for the Advancement of Science:

If, as is sometimes supposed, science consisted in nothing but the laborious accumulation of facts, it would soon come to a standstill, crushed, as it were, under its own weight. The suggestion of a new idea, or the detection of a law, supersedes much that has previously been a burden on the memory, and by introducing order and coherence facilitates the retention of the remainder in an available form. ( Rayleigh, 1885 , p. 20)

Indeed, when we write up our research, we are exhorted to “tell a story,” which achieves the “order and coherence” that Rayleigh described. Since his time, ample literature on narrative comprehension has confirmed that people fill in gaps in unstated information and find texts easier to comprehend and memorise when they fit a familiar narrative structure ( Bower & Morrow, 1990 ; Van den Broek, 1994 ).

This resonates with Dawkins’ ( 1976 ) criteria for a meme, i.e., an idea that persists by being transmitted from person to person. Memes need to be easy to remember, understand, and communicate, and so narrative accounts make far better memes than dry lists of facts. From this perspective, narrative serves a useful function in providing a scaffolding that facilitates communication. However, while this is generally a useful, and indeed essential, aspect of human cognition, in scientific communication, it can lead to propagation of false information. Bartlett (1932) noted that remembering is hardly ever really exact, “and it is not at all important that it should be so.” He was thinking of the beneficial aspects of schemata, in allowing us to avoid information overload and to focus on what is meaningful. However, as Dawkins emphasised, survival of a meme does not depend on it being useful or true. An idea such as the claim that vaccination causes autism is a very effective meme, but it has led to resurgence of diseases that were close to being eradicated.

In communicating scientific results, we need to strike a fine balance between presenting a precis of findings that is easily communicated and moving towards an increase in knowledge. I would argue the pendulum may have swung too far in the direction of encouraging researchers to tell good narratives. Not just media outlets, but also many journal editors and reviewers, encourage authors to tell simple stories that are easy to understand, and those who can produce these may be rewarded with funding and promotion.

The clearest illustration of narrative supplanting accurate reporting comes from the widespread use of HARKing, which was encouraged by Bem (2004) when he wrote,

There are two possible articles you can write: (a) the article you planned to write when you designed your study or (b) the article that makes the most sense now that you have seen the results. They are rarely the same, and the correct answer is (b).

Of course, formulating a hypothesis on the basis of observed data is a key part of the scientific process. However, as noted above, it is not acceptable to use the same data to both formulate and test the hypothesis—replication in a new sample is needed to avoid being misled by the play of chance and littering literature with false positives ( Lazic, 2016 ; Wagenmakers et al., 2012 ).

Kerr (1998) considered why HARKing is a successful strategy and pointed out that it allowed the researcher to construct an account of an experiment that fits a good story script:

Positing a theory serves as an effective “initiating event.” It gives certain events significance and justifies the investigators’ subsequent purposeful activities directed at the goal of testing the hypotheses. And, when one HARKs, a “happy ending” (i.e., confirmation) is guaranteed. (p. 203)

In this regard, Bem’s advice makes perfect sense: “A journal article tells a straightforward tale of a circumscribed problem in search of a solution. It is not a novel with subplots, flashbacks, and literary allusions, but a short story with a single linear narrative line.”

We have, then, a serious tension in scientific writing. We are expected to be scholarly and honest, to report all our data and analyses and not to hide inconvenient truths. At the same time, if we want people to understand and remember our work, we should tell a coherent story from which unnecessary details have been expunged and where we cut out any part of the narrative that distracts from the main conclusions.

Kerr (1998) was clear that HARKing has serious costs. As well as translating type I errors into hard-to-eradicate theory, he noted that it presents a distorted view of science as a process which is far less difficult and unpredictable than is really the case. We never learn what did not work because inconvenient results are suppressed. For early career researchers, it can lead to cynicism when they learn that the rosy picture portrayed in the literature was achieved only by misrepresentation.

Overcoming cognitive constraints to do better science

One thing that is clear from this overview is that we have known about cognitive constraints for decades, yet they continue to affect scientific research. Finding ways to mitigate the impact of these constraints should be a high priority for experimental psychologists. Here, I draw together some general approaches that might be used to devise an agenda for research improvement. Many of these ideas have been suggested before but without much consideration of cognitive constraints that may affect their implementation. Some methods, such as training, attempt to overcome the constraints directly in individuals: others involve making structural changes to how science is done to counteract our human tendency towards unscientific thinking. None of these provides a total solution: rather, the goal is to tweak the dials that dictate how people think and behave, to move us closer to better scientific practices.

It is often suggested that better training is needed to improve replicability of scientific results, yet the focus tends to be on formal instruction in experimental design and statistics. Less attention has been given to engendering a more intuitive understanding of probability, or counteracting cognitive biases, though there are exceptions, such as the course by Steel, Liermann, and Guttorp (2018) , which starts with a consideration of “How the wiring of the human brain leads to incorrect conclusions from data.” One way of inducing a more intuitive sense of statistics and p -values is by using data simulations. Simulation is not routinely incorporated in statistics training, but free statistical software now makes this within the grasp of all ( Tintle et al., 2015 ). This is a powerful way to experience how easy it is to get a “significant” p -value when running multiple tests. Students are often surprised when they generate repeated runs of a correlation matrix of random numbers with, say, five variables and find at least one “significant” correlation in about one in four runs. Once you understand that there is a difference between the probability associated with getting a specific result on a single test, predicted in advance, versus the probability of that result coming up at least once in a multitude of tests, then the dangers of p -hacking become easier to grasp.

Data simulation could also help overcome the misplaced “belief in the law of small numbers” ( Tversky & Kahneman, 1974 ). By generating datasets with a known effect size, and then taking samples from these and subjecting them to statistical test, the student can learn to appreciate just how easy it is to miss a true effect (type II error) if the study is underpowered.

There is small literature evaluating attempts to specifically inoculate people against certain types of cognitive bias. For instance, Morewedge et al. (2015) developed instructional videos and computer games designed to reduce a series of cognitive biases, including confirmation bias, and found these to be effective over the longer term. Typically, however, such interventions focus on hypothetical scenarios outside the scope of experimental psychology. They might improve scientific quality of research projects if adjusted to make them relevant to conducting and appraising experiments.

Triangulation of methods in study design

I noted above that for science to progress, we need to overcome a tendency to settle on the first theory that seems “good enough” to account for observations. Any method that forces the researcher to actively search for alternative explanations is, therefore, likely to stimulate better research.

The notion of triangulation ( Munafò & Davey Smith, 2018 ) was developed in the field of epidemiology, where reliance is primarily on observational data, and experimental manipulation is not feasible. Inferring causality from correlational data is hazardous, but it is possible to adopt a strategic approach of combining complementary approaches to analysis, each of which has different assumptions, strengths, and weaknesses. Epidemiology progresses when different explanations for correlational data are explicitly identified and evaluated, and converging evidence is obtained ( Lawlor, Tilling, & Davey Smith, 2016 ). This approach could be extended to other disciplines, by explicitly requiring researchers to use at least two different methods with different potential biases when evaluating a specific hypothesis.

A “culture of criticism”

Smith (2006) described peer review as “a flawed process, full of easily identified defects with little evidence that it works” (p. 182). Yet peer review provides one way of forcing researchers to recognise when they are so focused on a favoured theory that they are unable to break away. Hossenfelder (2018) has argued that the field of particle physics has stagnated because of a reluctance to abandon theories that are deemed “beautiful.” We are accustomed to regarding physicists as superior to psychologists in terms of theoretical and methodological sophistication. In general, they place far less emphasis than we do on statistical criteria for evidence, and where they do use statistics, they understand probability theory and adopt very stringent levels of significance. Nevertheless, according to Hossenfelder, they are subject to cognitive and social biases that make them reluctant to discard theories. She concludes her book with an Appendix on “What you can do to help,” and as well as advocating better understanding of cognitive biases, she recommends some cultural changes to address these. These include building “a culture of criticism.” In principle, we already have this—talks and seminars should provide a forum for research to be challenged—but in practice, critiquing another’s work is often seen as clashing with social conventions of being supportive to others, especially when it is conducted in public.

Recently, two other approaches have been developed, with the potential to make a “culture of criticism” more useful and more socially acceptable. Registered Reports ( Chambers, 2019 ) is an approach that was devised to prevent publication bias, p -hacking, and HARKing. This format moves the peer review process to a point before data collection so that results cannot influence editorial decisions. An unexpected positive consequence is that peer review comes at a point when it can be acted upon to improve the experimental design. Where reviewers of Registered Reports ask “how could we disprove the hypothesis?” and “what other explanations should we consider?” this can generate more informative experiments.

A related idea is borrowed from business practices and is known as the “pre mortem” approach ( Klein, 2007 ). Project developers gather together and are asked to imagine that a proposed project has gone ahead and failed. They are then encouraged to write down reasons why this has happened, allowing people to voice misgivings that they may have been reluctant to state openly, so they can be addressed before the project has begun. It would be worth evaluating the effectiveness of pre-mortems for scientific projects. We could strengthen this approach by incorporating ideas from Bang and Frith (2017) , who noted that group decision-making is most likely to be effective when the group is diverse and people can express their views anonymously. With both Registered Reports and the study pre-mortem, reviewers can have a role as critical friends who can encourage researchers to identify ways to improve a project before it is conducted. This can be a more positive experience for the reviewer, who may otherwise have no option but to recommend rejection of a study with flawed methodology.

Counteracting cherry-picking of literature

Turning to cherry-picking of prior literature, the established solution is the systematic review, where clear criteria are laid out in advance so that a comprehensive search can be made of all relevant studies ( Siddaway, Wood, & Hedges, 2019 ). The systematic review is only as good as the data that go into it, however, and if a field suffers from substantial publication bias and/or p -hacking, then, rather than tackling error entrenchment, it may add to it. With the most scrupulous search strategy, relevant papers with null results can be missed because positive results are mentioned in titles and abstracts of papers, whereas null results are not ( Lazic, 2016 , p. 15). This can mean that, if a study is looking at many possible associations (e.g., with brain regions or with genes), studies that considered a specific association but failed to find support for it will be systematically disregarded. This may explain why it seems to take 30 or 40 years for some erroneous entrenched theories to be abandoned. The situation may improve with increasing availability of open data. Provided data are adequately documented and accessible, the problem of missing relevant studies may be reduced.

Ultimately, the problem of biased reviews may not be soluble just by changing people’s citation habits. Journal editors and reviewers could insist that abstracts follow a structured format and report all variables that were tested, not just those that gave significant results. A more radical approach by funders may be needed to disrupt this wasteful cycle. When a research team applies to test a new idea, they could first be required to (a) conduct a systematic review (unless one has been recently done) and (b) replicate the original findings on which the work is based: this is the opposite to what happens currently, where novelty and originality are major criteria for funding. In addition, it could be made mandatory for any newly funded research idea to be investigated by at least two independent laboratories and using at least two different approaches (triangulation). All these measures would drastically slow down science and may be unfeasible where research needs highly specialised equipment, facilities, or skills that are specific to one laboratory. Nevertheless, slower science may be preferable to the current system where there are so many examples of false leads being pursued for decades, with consequent waste of resources.

Reconciling storytelling with honesty

Perhaps the hardest problem is how to reconcile our need for narrative with a “warts and all” account of research. Consider this advice from Bem (2004) —which I suspect many journal editors would endorse:

Contrary to the conventional wisdom, science does not care how clever or clairvoyant you were at guessing your results ahead of time. Scientific integrity does not require you to lead your readers through all your wrongheaded hunches only to show—voila!—they were wrongheaded. A journal article should not be a personal history of your stillborn thoughts . . . Your overriding purpose is to tell the world what you have learned from your study. If your results suggest a compelling framework for their presentation, adopt it and make the most instructive findings your centerpiece . . . Think of your dataset as a jewel. Your task is to cut and polish it, to select the facets to highlight, and to craft the best setting for it.

As Kerr (1998) pointed out, HARKing gives a misleading impression of what was found, which can be particularly damaging for students, who on reading literature may form the impression that it is normal for scientists to have their predictions confirmed and think of themselves as incompetent when their own experiments do not work out that way. One of the goals of pre-registration is to ensure that researchers do not omit inconvenient facts when writing up a study—or if they do, at least make it possible to see that this has been done. In the field of clinical medicine, impressive progress has been made in methodology, with registration now a requirement for clinical trials ( International Committee of Medical Journal Editors, 2019 ). Yet, Goldacre et al. (2019) found that even when a trial was registered, it was common for researchers to change the primary outcome measure without explanation, and it has been similarly noted that pre-registrations in psychology are often too ambiguous to preclude p -hacking ( Veldkamp et al., 2018 ). Registered Reports ( Chambers, 2019 ) adopt stricter standards that should prevent HARKing, but the author may struggle to maintain a strong narrative because messy reality makes a less compelling story than a set of results subjected to Bem’s (2004) cutting and polishing process.

Rewarding credible research practices

A final set of recommendations has to do with changing the culture so that incentives are aligned with efforts to counteract unhelpful cognitive constraints, and researchers are rewarded for doing reproducible, replicable research, rather than for grant income or publications in high-impact journals ( Forstmeier, Wagenmakers, & Parker, 2016 ; Pulverer, 2015 ). There is already evidence that funders are concerned to address problems with credibility of biomedical research ( Academy of Medical Sciences, 2015 ), and rigour and reproducibility are increasingly mentioned in grant guidelines (e.g., https://grants.nih.gov/policy/reproducibility/index.htm ). One funder, Cancer Research UK, is innovating by incorporating Registered Reports in a two-stage funding model ( Munafò, 2017 ). We now need publishers and institutions to follow suit and ensure that researchers are not disadvantaged by adopting a self-critical mind-set and engaging in practices of open and reproducible science ( Poldrack, 2019 ).

Acknowledgments

My thanks to Kate Nation, Matt Jaquiery, Joe Chislett, Laura Fortunato, Uta Frith, Stefan Lewandowsky, and Karalyn Patterson for invaluable comments on an early draft of this manuscript.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The author is supported by a Principal Research Fellowship from the Wellcome Trust (programme grant no. 082498) and European Research Council advanced grant no. 694189.

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What Are The Steps Of The Scientific Method?

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Science is not just knowledge. It is also a method for obtaining knowledge. Scientific understanding is organized into theories.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

It involves careful observation, asking questions, formulating hypotheses, experimental testing, and refining hypotheses based on experimental findings.

How it is Used

The scientific method can be applied broadly in science across many different fields, such as chemistry, physics, geology, and psychology. In a typical application of this process, a researcher will develop a hypothesis, test this hypothesis, and then modify the hypothesis based on the outcomes of the experiment.

The process is then repeated with the modified hypothesis until the results align with the observed phenomena. Detailed steps of the scientific method are described below.

Keep in mind that the scientific method does not have to follow this fixed sequence of steps; rather, these steps represent a set of general principles or guidelines.

7 Steps of the Scientific Method

Psychology uses an empirical approach.

Empiricism (founded by John Locke) states that the only source of knowledge comes through our senses – e.g., sight, hearing, touch, etc.

Empirical evidence does not rely on argument or belief. Thus, empiricism is the view that all knowledge is based on or may come from direct observation and experience.

The empiricist approach of gaining knowledge through experience quickly became the scientific approach and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.

Step 1: Make an Observation (Theory Construction)

Every researcher starts at the very beginning. Before diving in and exploring something, one must first determine what they will study – it seems simple enough!

By making observations, researchers can establish an area of interest. Once this topic of study has been chosen, a researcher should review existing literature to gain insight into what has already been tested and determine what questions remain unanswered.

This assessment will provide helpful information about what has already been comprehended about the specific topic and what questions remain, and if one can go and answer them.

Specifically, a literature review might implicate examining a substantial amount of documented material from academic journals to books dating back decades. The most appropriate information gathered by the researcher will be shown in the introduction section or abstract of the published study results.

The background material and knowledge will help the researcher with the first significant step in conducting a psychology study, which is formulating a research question.

This is the inductive phase of the scientific process. Observations yield information that is used to formulate theories as explanations. A theory is a well-developed set of ideas that propose an explanation for observed phenomena.

Inductive reasoning moves from specific premises to a general conclusion. It starts with observations of phenomena in the natural world and derives a general law.

Step 2: Ask a Question

Once a researcher has made observations and conducted background research, the next step is to ask a scientific question. A scientific question must be defined, testable, and measurable.

A useful approach to develop a scientific question is: “What is the effect of…?” or “How does X affect Y?”

To answer an experimental question, a researcher must identify two variables: the independent and dependent variables.

The independent variable is the variable manipulated (the cause), and the dependent variable is the variable being measured (the effect).

An example of a research question could be, “Is handwriting or typing more effective for retaining information?” Answering the research question and proposing a relationship between the two variables is discussed in the next step.

Step 3: Form a Hypothesis (Make Predictions)

A hypothesis is an educated guess about the relationship between two or more variables. A hypothesis is an attempt to answer your research question based on prior observation and background research. Theories tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

For example, a researcher might ask about the connection between sleep and educational performance. Do students who get less sleep perform worse on tests at school?

It is crucial to think about different questions one might have about a particular topic to formulate a reasonable hypothesis. It would help if one also considered how one could investigate the causalities.

It is important that the hypothesis is both testable against reality and falsifiable. This means that it can be tested through an experiment and can be proven wrong.

The falsification principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.

To test a hypothesis, we first assume that there is no difference between the populations from which the samples were taken. This is known as the null hypothesis and predicts that the independent variable will not influence the dependent variable.

Examples of “if…then…” Hypotheses:

If one gets less than 6 hours of sleep, then one will do worse on tests than if one obtains more rest.
If one drinks lots of water before going to bed, one will have to use the bathroom often at night.
If one practices exercising and lighting weights, then one’s body will begin to build muscle.

The research hypothesis is often called the alternative hypothesis and predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Although one could state and write a scientific hypothesis in many ways, hypotheses are usually built like “if…then…” statements.

Step 4: Run an Experiment (Gather Data)

The next step in the scientific method is to test your hypothesis and collect data. A researcher will design an experiment to test the hypothesis and gather data that will either support or refute the hypothesis.

The exact research methods used to examine a hypothesis depend on what is being studied. A psychologist might utilize two primary forms of research, experimental research, and descriptive research.

The scientific method is objective in that researchers do not let preconceived ideas or biases influence the collection of data and is systematic in that experiments are conducted in a logical way.

Experimental Research

Experimental research is used to investigate cause-and-effect associations between two or more variables. This type of research systematically controls an independent variable and measures its effect on a specified dependent variable.

Experimental research involves manipulating an independent variable and measuring the effect(s) on the dependent variable. Repeating the experiment multiple times is important to confirm that your results are accurate and consistent.

One of the significant advantages of this method is that it permits researchers to determine if changes in one variable cause shifts in each other.

While experiments in psychology typically have many moving parts (and can be relatively complex), an easy investigation is rather fundamental. Still, it does allow researchers to specify cause-and-effect associations between variables.

Most simple experiments use a control group, which involves those who do not receive the treatment, and an experimental group, which involves those who do receive the treatment.

An example of experimental research would be when a pharmaceutical company wants to test a new drug. They give one group a placebo (control group) and the other the actual pill (experimental group).

Descriptive Research

Descriptive research is generally used when it is challenging or even impossible to control the variables in question. Examples of descriptive analysis include naturalistic observation, case studies , and correlation studies .

One example of descriptive research includes phone surveys that marketers often use. While they typically do not allow researchers to identify cause and effect, correlational studies are quite common in psychology research. They make it possible to spot associations between distinct variables and measure the solidity of those relationships.

Step 5: Analyze the Data and Draw Conclusions

Once a researcher has designed and done the investigation and collected sufficient data, it is time to inspect this gathered information and judge what has been found. Researchers can summarize the data, interpret the results, and draw conclusions based on this evidence using analyses and statistics.

Upon completion of the experiment, you can collect your measurements and analyze the data using statistics. Based on the outcomes, you will either reject or confirm your hypothesis.

Analyze the Data

So, how does a researcher determine what the results of their study mean? Statistical analysis can either support or refute a researcher’s hypothesis and can also be used to determine if the conclusions are statistically significant.

When outcomes are said to be “statistically significant,” it is improbable that these results are due to luck or chance. Based on these observations, investigators must then determine what the results mean.

An experiment will support a hypothesis in some circumstances, but sometimes it fails to be truthful in other cases.

What occurs if the developments of a psychology investigation do not endorse the researcher’s hypothesis? It does mean that the study was worthless. Simply because the findings fail to defend the researcher’s hypothesis does not mean that the examination is not helpful or instructive.

This kind of research plays a vital role in supporting scientists in developing unexplored questions and hypotheses to investigate in the future. After decisions have been made, the next step is to communicate the results with the rest of the scientific community.

This is an integral part of the process because it contributes to the general knowledge base and can assist other scientists in finding new research routes to explore.

If the hypothesis is not supported, a researcher should acknowledge the experiment’s results, formulate a new hypothesis, and develop a new experiment.

We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist that could refute a theory.

Draw Conclusions and Interpret the Data

When the empirical observations disagree with the hypothesis, a number of possibilities must be considered. It might be that the theory is incorrect, in which case it needs altering, so it fully explains the data.

Alternatively, it might be that the hypothesis was poorly derived from the original theory, in which case the scientists were expecting the wrong thing to happen.

It might also be that the research was poorly conducted, or used an inappropriate method, or there were factors in play that the researchers did not consider. This will begin the process of the scientific method again.

If the hypothesis is supported, the researcher can find more evidence to support their hypothesis or look for counter-evidence to strengthen their hypothesis further.

In either scenario, the researcher should share their results with the greater scientific community.

Step 6: Share Your Results

One of the final stages of the research cycle involves the publication of the research. Once the report is written, the researcher(s) may submit the work for publication in an appropriate journal.

Usually, this is done by writing up a study description and publishing the article in a professional or academic journal. The studies and conclusions of psychological work can be seen in peer-reviewed journals such as Developmental Psychology , Psychological Bulletin, the Journal of Social Psychology, and numerous others.

Scientists should report their findings by writing up a description of their study and any subsequent findings. This enables other researchers to build upon the present research or replicate the results.

As outlined by the American Psychological Association (APA), there is a typical structure of a journal article that follows a specified format. In these articles, researchers:

Supply a brief narrative and background on previous research
Give their hypothesis
Specify who participated in the study and how they were chosen
Provide operational definitions for each variable
Explain the measures and methods used to collect data
Describe how the data collected was interpreted
Discuss what the outcomes mean

A detailed record of psychological studies and all scientific studies is vital to clearly explain the steps and procedures used throughout the study. So that other researchers can try this experiment too and replicate the results.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound. Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

This last step is important because all results, whether they supported or did not support the hypothesis, can contribute to the scientific community. Publication of empirical observations leads to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound.

Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

By replicating studies, psychologists can reduce errors, validate theories, and gain a stronger understanding of a particular topic.

Step 7: Repeat the Scientific Method (Iteration)

Now, if one’s hypothesis turns out to be accurate, find more evidence or find counter-evidence. If one’s hypothesis is false, create a new hypothesis or try again.

One may wish to revise their first hypothesis to make a more niche experiment to design or a different specific question to test.

The amazingness of the scientific method is that it is a comprehensive and straightforward process that scientists, and everyone, can utilize over and over again.

So, draw conclusions and repeat because the scientific method is never-ending, and no result is ever considered perfect.

The scientific method is a process of:

Making an observation.
Forming a hypothesis.
Making a prediction.
Experimenting to test the hypothesis.

The procedure of repeating the scientific method is crucial to science and all fields of human knowledge.

Further Information

Karl Popper – Falsification
Thomas – Kuhn Paradigm Shift
Positivism in Sociology: Definition, Theory & Examples
Is Psychology a Science?
Psychology as a Science (PDF)

List the 6 steps of the scientific methods in order

Make an observation (theory construction)
Ask a question. A scientific question must be defined, testable, and measurable.
Form a hypothesis (make predictions)
Run an experiment to test the hypothesis (gather data)
Analyze the data and draw conclusions
Share your results so that other researchers can make new hypotheses

What is the first step of the scientific method?

The first step of the scientific method is making an observation. This involves noticing and describing a phenomenon or group of phenomena that one finds interesting and wishes to explain.

Observations can occur in a natural setting or within the confines of a laboratory. The key point is that the observation provides the initial question or problem that the rest of the scientific method seeks to answer or solve.

What is the scientific method?

The scientific method is a step-by-step process that investigators can follow to determine if there is a causal connection between two or more variables.

Psychologists and other scientists regularly suggest motivations for human behavior. On a more casual level, people judge other people’s intentions, incentives, and actions daily.

While our standard assessments of human behavior are subjective and anecdotal, researchers use the scientific method to study psychology objectively and systematically.

All utilize a scientific method to study distinct aspects of people’s thinking and behavior. This process allows scientists to analyze and understand various psychological phenomena, but it also provides investigators and others a way to disseminate and debate the results of their studies.

The outcomes of these studies are often noted in popular media, which leads numerous to think about how or why researchers came to the findings they did.

Why Use the Six Steps of the Scientific Method

The goal of scientists is to understand better the world that surrounds us. Scientific research is the most critical tool for navigating and learning about our complex world.

Without it, we would be compelled to rely solely on intuition, other people’s power, and luck. We can eliminate our preconceived concepts and superstitions through methodical scientific research and gain an objective sense of ourselves and our world.

All psychological studies aim to explain, predict, and even control or impact mental behaviors or processes. So, psychologists use and repeat the scientific method (and its six steps) to perform and record essential psychological research.

So, psychologists focus on understanding behavior and the cognitive (mental) and physiological (body) processes underlying behavior.

In the real world, people use to understand the behavior of others, such as intuition and personal experience. The hallmark of scientific research is evidence to support a claim.

Scientific knowledge is empirical, meaning it is grounded in objective, tangible evidence that can be observed repeatedly, regardless of who is watching.

The scientific method is crucial because it minimizes the impact of bias or prejudice on the experimenter. Regardless of how hard one tries, even the best-intentioned scientists can’t escape discrimination. can’t

It stems from personal opinions and cultural beliefs, meaning any mortal filters data based on one’s experience. Sadly, this “filtering” process can cause a scientist to favor one outcome over another.

For an everyday person trying to solve a minor issue at home or work, succumbing to these biases is not such a big deal; in fact, most times, it is important.

But in the scientific community, where results must be inspected and reproduced, bias or discrimination must be avoided.

When to Use the Six Steps of the Scientific Method ?

One can use the scientific method anytime, anywhere! From the smallest conundrum to solving global problems, it is a process that can be applied to any science and any investigation.

Even if you are not considered a “scientist,” you will be surprised to know that people of all disciplines use it for all kinds of dilemmas.

Try to catch yourself next time you come by a question and see how you subconsciously or consciously use the scientific method.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Overview of the Scientific Method

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Using Science to Inform Educational Practices

Experimental Research

As you’ve learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hairstyle or new food. However, in the scientific context, an experiment has precise requirements for design and implementation.

Video 2.8.1. Experimental Research Design provides explanation and examples for correlational research. A closed-captioned version of this video is available here .

The Experimental Hypothesis

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon. Seeing behavior like this right after a child watches violent television programming might lead you to hypothesize that viewing violent television programming leads to an increase in the display of violent behaviors. These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to test our hypothesis rigorously. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to define precisely, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts are recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that the participants are unaware as to which group they are in (experiment or control group) while the researcher knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect , you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case, a sugar pill). Now everyone gets a pill, and once again, neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations.

Video 2.8.2. Introduction to Experimental Design introduces fundamental elements for experimental research design.

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 2.3). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

Figure 2.8.1. In an experiment, manipulations of the independent variable are expected to result in changes in the dependent variable.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half-hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants, so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 2.4). If possible, we should use a random sample (there are other types of samples, but for the purposes of this chapter, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead, we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth-graders that we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Figure 2.8.2. Researchers may work with (a) a large population or (b) a sample group that is a subset of the larger population.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be improbable that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Exercise 2.2 Randomization in Sampling and Assignment

Use this online tool to generate randomized numbers instantly and to learn more about random sampling and assignments.

Issues to Consider

Interpreting Experimental Findings

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because of random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Candela Citations

Experimental Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/experimental-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Experimental Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/experimental-research/. Project : https://courses.lumenlearning.com/adolescent/chapter/experimental-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Common Methodologies in GSEP Research

Intro to Methodologies
Additional Resources

Common Methodologies found in Graduate Psych Research

Welcome,

This guide aims to provide an overview of various research methodologies most frequently encountered in graduate psychology research studies.

Methodologies

1. Experimental Methodology:

The experimental method involves manipulating one variable (independent variable) to observe the effect it has on another variable (dependent variable), while controlling for extraneous variables. It is used to establish cause-and-effect relationships between variables in controlled laboratory settings.

Key Concepts: Randomization, Control Group, Experimental Group, Internal Validity.

2. Survey Methodology:

Description: Surveys involve collecting data from a sample of individuals through questionnaires or interviews, with the aim of generalizing the findings to a larger population. It is commonly used in psychology to gather information on attitudes, behaviors, and opinions from diverse populations.

Key Concepts: Sampling Techniques, Questionnaire Design, Reliability, Validity.

3. Observational Methodology:

Description: Observational studies involve systematically observing and recording behavior in naturalistic settings without intervening or manipulating variables. This method is used to study behavior in real-world contexts, offering insights into naturally occurring phenomena.

Key Concepts: Participant Observation, Non-Participant Observation, Ethnography, Observer Bias.

4. Case Study Methodology:

Description: Case studies involve in-depth examination of a single individual, group, or phenomenon, utilizing various data sources such as interviews, observations, and archival records. Case studies are valuable for exploring complex or rare phenomena in-depth, providing detailed insights into specific cases.

Key Concepts: Rich Description, Longitudinal Analysis, Generalization.

5. Correlational Methodology:

Description: Correlational studies examine the relationship between two or more variables without manipulating them, focusing on the extent and direction of their association. This method identifies patterns and associations between variables, informing predictions and further research directions.

Key Concepts: Correlation Coefficient, Directionality, Third Variable Problem.

6. Qualitative Methodology:

Description: Qualitative research focuses on understanding and interpreting subjective experiences, meanings, and social processes through methods such as interviews, focus groups, and textual analysis. The qualitative method provides nuanced insights into individuals' perspectives, cultural contexts, and social phenomena, often used in exploratory or theory-building research.

Key Concepts: Thematic Analysis, Grounded Theory, Reflexivity, Saturation.

7. Mixed Methods:

Description: Mixed methods research combines qualitative and quantitative approaches within a single study, allowing researchers to triangulate findings, enhance validity, and gain comprehensive understanding. Mixed methods offer the flexibility to address complex research questions by leveraging the strengths of both qualitative and quantitative methodologies.

Key Concepts: Integration, Sequential Design, Convergence, Expansion.

8. Quantitative Methodology:

Description: Quantitative research involves collecting and analyzing numerical data to test hypotheses, identify patterns, and quantify relationships between variables using statistical techniques. This method is widely used in psychology to investigate relationships, trends, and causal effects through numerical data analysis.

Key Concepts: Hypothesis Testing, Descriptive Statistics, Inferential Statistics, Measurement Scales.

9. Longitudinal Methodology:

Description: Longitudinal studies involve collecting data from the same participants over an extended period, allowing researchers to observe changes and trajectories of variables over time. Longitudinal studies are used to investigate developmental processes, life transitions, and long-term effects of interventions or treatments in psychology.

Key Concepts: Panel Designs, Cohort Studies, Attrition, Retention Strategies.

Next: Additional Resources >>
Last Updated: Apr 24, 2024 11:48 AM
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How Does Experimental Psychology Study Behavior?

Purpose, methods, and history

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

Why It Matters

What factors influence people's behaviors and thoughts? Experimental psychology utilizes scientific methods to answer these questions by researching the mind and behavior. Experimental psychologists conduct experiments to learn more about why people do certain things.

Overview of Experimental Psychology

Why do people do the things they do? What factors influence how personality develops? And how do our behaviors and experiences shape our character?

These are just a few of the questions that psychologists explore, and experimental methods allow researchers to create and empirically test hypotheses. By studying such questions, researchers can also develop theories that enable them to describe, explain, predict, and even change human behaviors.

For example, researchers might utilize experimental methods to investigate why people engage in unhealthy behaviors. By learning more about the underlying reasons why these behaviors occur, researchers can then search for effective ways to help people avoid such actions or replace unhealthy choices with more beneficial ones.

Why Experimental Psychology Matters

While students are often required to take experimental psychology courses during undergraduate and graduate school , think about this subject as a methodology rather than a singular area within psychology. People in many subfields of psychology use these techniques to conduct research on everything from childhood development to social issues.

Experimental psychology is important because the findings play a vital role in our understanding of the human mind and behavior.

By better understanding exactly what makes people tick, psychologists and other mental health professionals can explore new approaches to treating psychological distress and mental illness. These are often topics of experimental psychology research.

Experimental Psychology Methods

So how exactly do researchers investigate the human mind and behavior? Because the mind is so complex, it seems like a challenging task to explore the many factors that contribute to how we think, act, and feel.

Experimental psychologists use a variety of different research methods and tools to investigate human behavior. Methods in the experimental psychology category include experiments, case studies, correlational research, and naturalistic observations.

Experiments

Experimentation remains the primary standard in psychological research. In some cases, psychologists can perform experiments to determine if there is a cause-and-effect relationship between different variables.

The basics of conducting a psychology experiment involve:

Randomly assigning participants to groups
Operationally defining variables
Developing a hypothesis
Manipulating independent variables
Measuring dependent variables

One experimental psychology research example would be to perform a study to look at whether sleep deprivation impairs performance on a driving test. The experimenter could control other variables that might influence the outcome, varying the amount of sleep participants get the night before.

All of the participants would then take the same driving test via a simulator or on a controlled course. By analyzing the results, researchers can determine if changes in the independent variable (amount of sleep) led to differences in the dependent variable (performance on a driving test).

Case Studies

Case studies allow researchers to study an individual or group of people in great depth. When performing a case study, the researcher collects every single piece of data possible, often observing the person or group over a period of time and in a variety of situations. They also collect detailed information about their subject's background—including family history, education, work, and social life—is also collected.

Such studies are often performed in instances where experimentation is not possible. For example, a scientist might conduct a case study when the person of interest has had a unique or rare experience that could not be replicated in a lab.

Correlational Research

Correlational studies are an experimental psychology method that makes it possible for researchers to look at relationships between different variables. For example, a psychologist might note that as one variable increases, another tends to decrease.

While such studies can look at relationships, they cannot be used to imply causal relationships. The golden rule is that correlation does not equal causation.

Naturalistic Observations

Naturalistic observation gives researchers the opportunity to watch people in their natural environments. This experimental psychology method can be particularly useful in cases where the investigators believe that a lab setting might have an undue influence on participant behaviors.

What Experimental Psychologists Do

Experimental psychologists work in a wide variety of settings, including colleges, universities, research centers, government, and private businesses. Some of these professionals teach experimental methods to students while others conduct research on cognitive processes, animal behavior, neuroscience, personality, and other subject areas.

Those who work in academic settings often teach psychology courses in addition to performing research and publishing their findings in professional journals. Other experimental psychologists work with businesses to discover ways to make employees more productive or to create a safer workplace—a specialty area known as human factors psychology .

Experimental Psychology Research Examples

Some topics that might be explored in experimental psychology research include how music affects motivation, the impact social media has on mental health , and whether a certain color changes one's thoughts or perceptions.

History of Experimental Psychology

To understand how experimental psychology got where it is today, it can be helpful to look at how it originated. Psychology is a relatively young discipline, emerging in the late 1800s. While it started as part of philosophy and biology, it officially became its own field of study when early psychologist Wilhelm Wundt founded the first laboratory devoted to the study of experimental psychology.

Some of the important events that helped shape the field of experimental psychology include:

1874 - Wilhelm Wundt published the first experimental psychology textbook, "Grundzüge der physiologischen Psychologie" ("Principles of Physiological Psychology").
1875 - William James opened a psychology lab in the United States. The lab was created for the purpose of class demonstrations rather than to perform original experimental research.
1879 - The first experimental psychology lab was founded in Leipzig, Germany. Modern experimental psychology dates back to the establishment of the very first psychology lab by pioneering psychologist Wilhelm Wundt during the late nineteenth century.
1883 - G. Stanley Hall opened the first experimental psychology lab in the United States at John Hopkins University.
1885 - Herman Ebbinghaus published his famous "Über das Gedächtnis" ("On Memory"), which was later translated to English as "Memory: A Contribution to Experimental Psychology." In the work, Ebbinghaus described learning and memory experiments that he conducted on himself.
1887 - George Truball Ladd published his textbook "Elements of Physiological Psychology," the first American book to include a significant amount of information on experimental psychology.
1887 - James McKeen Cattell established the world's third experimental psychology lab at the University of Pennsylvania.
1890 - William James published his classic textbook, "The Principles of Psychology."
1891 - Mary Whiton Calkins established an experimental psychology lab at Wellesley College, becoming the first woman to form a psychology lab.
1893 - G. Stanley Hall founded the American Psychological Association , the largest professional and scientific organization of psychologists in the United States.
1920 - John B. Watson and Rosalie Rayner conducted their now-famous Little Albert Experiment , in which they demonstrated that emotional reactions could be classically conditioned in people.
1929 - Edwin Boring's book "A History of Experimental Psychology" was published. Boring was an influential experimental psychologist who was devoted to the use of experimental methods in psychology research.
1955 - Lee Cronbach published "Construct Validity in Psychological Tests," which popularized the use of construct validity in psychological studies.
1958 - Harry Harlow published "The Nature of Love," which described his experiments with rhesus monkeys on attachment and love.
1961 - Albert Bandura conducted his famous Bobo doll experiment, which demonstrated the effects of observation on aggressive behavior.

Experimental Psychology Uses

While experimental psychology is sometimes thought of as a separate branch or subfield of psychology, experimental methods are widely used throughout all areas of psychology.

Developmental psychologists use experimental methods to study how people grow through childhood and over the course of a lifetime.
Social psychologists use experimental techniques to study how people are influenced by groups.
Health psychologists rely on experimentation and research to better understand the factors that contribute to wellness and disease.

A Word From Verywell

The experimental method in psychology helps us learn more about how people think and why they behave the way they do. Experimental psychologists can research a variety of topics using many different experimental methods. Each one contributes to what we know about the mind and human behavior.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS. Research Methods in Psychology . McGraw-Hill.

Heale R, Twycross A. What is a case study? . Evid Based Nurs. 2018;21(1):7-8. doi:10.1136/eb-2017-102845

Chiang IA, Jhangiani RS, Price PC. Correlational research . In: Research Methods in Psychology, 2nd Canadian edition. BCcampus Open Education.

Pierce T. Naturalistic observation . Radford University.

Kantowitz BH, Roediger HL, Elmes DG. Experimental Psychology . Cengage Learning.

Weiner IB, Healy AF, Proctor RW. Handbook of Psychology: Volume 4, Experimental Psychology . John Wiley & Sons.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Published: 23 April 2024

People are surprisingly hesitant to reach out to old friends

Lara B. Aknin ORCID: orcid.org/0000-0003-1712-6542 1 na1 &
Gillian M. Sandstrom ORCID: orcid.org/0000-0002-0549-9600 2 na1

Communications Psychology volume 2 , Article number: 34 ( 2024 ) Cite this article

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Human behaviour

Social relationships provide one of the most reliable paths to happiness, but relationships can fade for various reasons. While it does not take much to reinitiate contact, here we find that people are surprisingly reluctant to do so. Specifically, most people reported losing touch with an old friend yet expressed little interest in reaching out (Studies 1-2, N s = 401 and 199). Moreover, fewer than one third of participants sent a message to an old friend, even when they wanted to, thought the friend would be appreciative, had the friend’s contact information, and were given time to draft and send a message (Studies 3-4, N s = 453 and 604). One reason for this reluctance may be that old friends feel like strangers. Supporting this possibility, participants were no more willing to reach out to an old friend than they were to talk to a stranger (Study 5, N = 288), and were less willing to contact old friends who felt more like strangers (Study 6, N = 319). Therefore, in Study 7 ( N = 194), we adapted an intervention shown to ease anxieties about talking to strangers and found that it increased the number of people who reached out to an old friend by two-thirds.

Worldwide divergence of values

Adults who microdose psychedelics report health related motivations and lower levels of anxiety and depression compared to non-microdosers

Anger is eliminated with the disposal of a paper written because of provocation

Introduction.

Evidence from across the social sciences demonstrates that social relationships provide one of the most robust and reliable routes to well-being. For instance, individuals with strong and satisfactory relationships report the highest levels of happiness 1 , 2 , and people who have someone to count on in times of need report higher life evaluations worldwide 3 .

While the quality of relationships matters, so too do the quantity and diversity of social connections. Social network size is positively associated with greater well-being 4 , 5 and recent work spanning multiple international data sets indicates that people who have more diverse relationship networks also report greater well-being 6 . These findings align with recent theorizing in relationship science which cautions against relying on any one person to fulfill all of one’s emotional needs 7 . Instead, people who turn to different social connections for different emotion regulation needs (e.g., calling on one person to cheer them up when they are sad, and a different person to calm them down when they are anxious) report higher well-being 8 . Thus, although classic work indicates that high-quality relationships are necessary for happiness 1 , 9 , recent research suggests that having more diverse relationships is also a predictor of well-being.

People recognize that relationships are an important source of personal meaning and well-being 10 , 11 , yet life can get busy and compel various relationships to fade or be put on hold. The high priority placed on work and productivity in North America has led people to cut back on social connections and social time to meet increasing demands at work 12 , 13 . Indeed, a significant majority of working Americans feel as if they do not have enough time in the day 14 . Social withdrawal may also occur in more discrete episodes, such as when people navigate life transitions to parenthood or a new job, and contributes to elevated feelings of loneliness during these pivotal times 15 , 16 , 17 .

While the strength of friendships may naturally wax and wane 18 , neglecting relationships for too long can be problematic. Loneliness is defined as a perceived lack of social connection, and it predicts a range of mental and physical health challenges 19 , 20 . Given the clear importance of social connection, Sociometer Theory 21 posits that self esteem functions as a psychological gauge to indicate the extent to which one feels accepted and socially valued. This gauge alerts people when social connection levels decline too far, and compels people to prioritize and strengthen social relationships. But how is one to do so?

Reaching out to an old friend with whom one has lost touch offers one accessible and viable channel for bolstering and diversifying social connection. For instance, a person could visit, call, email or send a text message to a friend, colleague, or family member that they like and care about but have not seen in some time (which we refer to as an “old friend”). Such efforts to reconnect are likely more efficient than initiating a new friendship; research estimates that it takes more than 200 hours of contact to turn a new acquaintance into a close friend 22 . This may be why empirically-informed programs, such as Groups4Health, recommend that individuals who are lonely consider reconnecting with old friends 23 . Moreover, research suggests that reaching out to an old friend can be beneficial. One study that asked MBA students to solicit help or advice on a work project found that reconnecting with “dormant ties” provided more useful knowledge and insight than connecting with current strong ties 24 .

While reaching out to old friends may be practical, this strategy may not be enacted because various psychological hurdles hinder people’s willingness to connect with others 25 . Indeed, recent work indicates that people overestimate the awkwardness of reaching out to an old friend and underestimate the appreciation and positive feelings such acts foster 26 , 27 , 28 . In addition, people misestimate the outcomes of other social acts involving other social partners. For instance, although talking to strangers can boost short-term happiness, people expect the opposite 29 , 30 . Similarly, people systematically overestimate how uncomfortable it will be to express gratitude and fail to recognize how much a compliment means to the recipient 31 , 32 , 33 . Collectively these findings indicate that people hold a number of faulty assumptions about the realities and consequences of various social interactions.

Critically, systematically underestimating others’ appreciation for one’s social behaviour (e.g., reaching out, talking to a stranger, giving a compliment) is expected to make people more reluctant to engage in these activities because they are missing the full motivation to act. While this premise is grounded in common sense and psychological theory 34 , additional research is needed to test the extent to which these misestimations translate into refraining from engaging in the behaviour. One study attempted to increase the number of people reaching out to an old friend by teaching participants about misestimation errors 28 . Unfortunately, this intervention did not translate into more people actually reaching out to an old friend. Thus, while past work has demonstrated that people systematically underappreciate how much social targets value interactions, including being contacted by an old friend, here we explore people’s self-reported, and actual willingness to engage in these actions, as well as how to promote this valuable behaviour.

Are people reluctant to reach out to old friends, why might this be, and how can they be encouraged to reconnect? We examine these questions in seven studies. In Study 1, we ask what proportion of people have lost touch with an old friend, how willing they are to reach out, what barriers restrain them, and what reasons would encourage them to reach out. After observing a general reluctance to reach out, in Study 2 we investigate whether people are hesitant about the idea of reconnecting with an old friend or simply aversive to the idea of being the one to reach out. Then, in Studies 3 and 4, we test multiple interventions designed to address some of the barriers identified in Study 1. These efforts have little influence on the proportion of people who actually reach out to an old friend when given the opportunity to do so.

In light of these data, we reasoned that one explanation for why people may be reluctant to reach out to old friends is because old friends may feel like strangers once substantial time has passed. Consistent with this possibility, several of the barriers that participants endorsed when thinking about reaching out to old friends are similar to the barriers that make people reluctant to talk to strangers. Therefore, in Study 5 we benchmark people’s willingness to reconnect with an old friend against several daily behaviours, including talking to a stranger. In finding some evidence that reaching out to an old friend may be psychologically similar to talking to a stranger, in Study 6 we examine whether people are more reluctant to reach out to old friends when those friends feel more like strangers. Finally, in Study 7, we take lessons from an intervention that has lastingly eased anxieties about talking to strangers. By applying a similar design in which we target participants’ behaviour rather than their attitudes, we effectively encourage more people to reach out to old friends. Two additional studies are included in the Supplementary Information ( SI) : Supplementary Note Study S 8 ; Supplementary Note Study S 9 .

Studies 1, 5, and S8-9 were approved by the Office of Research Ethics at Simon Fraser University (application numbers: 30000726, 30001307, 30001308, and 30002053, respectively). Studies 2, 3, 4, 6, and 7 were approved by the Sciences & Technology Cross-Schools Research Ethics Committee at the University of Sussex (application numbers: ER/GS474/1, ER/GS474/3, ER/GS474/6, ER/GS474/9, and ER/GS474/10, respectively). All participants provided informed consent before participation and studies were conducted in compliance with all relevant ethical guidelines. Studies 2–7 and both supplemental studies in the Supporting Information were pre-registered. Pre-registration links and the dates they were posted are as follows: Study 2 (osf.io/93mwh; April 13, 2022), Study 3 (osf.io/ynt63; July 20, 2022), Study 4 (osf.io/npwa4; December 11, 2022), Study 5 (osf.io/bm3x7; February 10, 2023), Study 6 (osf.io/phrc9; October 16, 2023), Study 7 (osf.io/rzpu8; November 14, 2023), Study S8 in SI (aspredicted.org/q4gj8.pdf; September 13, 2022), and Study S9 in SI (aspredicted.org/bq7cs.pdf; October 3, 2023). We deviated from the Study 2 pre-registration in that we had originally planned to recruit 200 young adults and 200 older adults for this study, but ultimately only young adults participated. For clarity and transparency, we report the results of all pre-registered hypotheses in the main text. In the Methods section, we fully describe the measures that correspond to the results that are reported in the main text, and then name any additional variables that were not analyzed, which can be viewed in the materials on OSF.

All data were collected on Qualtrics, and random assignment to condition (i.e., in Studies 2, 3, 4, and 7) was done by Qualtrics. Participants self-reported their gender in each study. In all studies, data distributions were assumed to be normal but this was not formally tested. All samples were convenience samples, except for Study S8 in which we collected data from a nationally representative sample of Americans.

Participants

Four-hundred forty-one undergraduates at a university in Canada participated as part of a larger study in exchange for course credit. Of these, 40 (9%) had never lost touch with someone, and were not invited to continue with the survey. This left a final sample of 401 participants ( M age = 19.2, SD = 2.0; 305 women, 86 men, 10 other). Sample size calculations were conducted a priori for a separate research question.

Participants completed an online survey in a private room. After answering several questions about an unrelated topic, they were asked to indicate whether they had “lost touch with a friend that [they] care about.” If yes, participants were asked to provide their old friend’s initials to personalize the following questions. If no, participants did not complete the remaining questions, and were not included in the analyses.

Participants were asked how willing they would be to reach out to their old friend via phone, text, or email to say hello, both in general (i.e., with no timeframe specified) and right now, on a Likert scale with anchor labels: 1 = not at all , 4 = neutral/undecided , 7 = extremely .

Because expectations about the recipient’s response are likely to impact how willing someone may be to reach out 28 , 35 , 36 , we asked participants two questions about how positively their friend would evaluate them and their message if they were to reach out (1 = very negative to 7 = very positive ).

We asked participants to what extent each of the following barriers held them back from reconnecting with the friend in question or other friends they have not been in touch with for a while, using a 7-point scale (1 = not at all relevant , 7 = extremely relevant ). The potential barriers were: (i) my time is limited, (ii) their time is limited, (iii) I don’t have time for a longer catch-up right now, (iv) I don’t have anything important to say, (v) I’m not sure I’ll get the wording just right, (vi) it would be awkward to reach out after all this time, (vii) I don’t know if they are interested in hearing from me, and (viii) I don’t want to bother them. Participants could also type another reason for not reaching out, if desired.

Finally, we asked participants to what extent they would be willing to reach out to their old friend for each of the following reasons (1 = not at all , 7 = extremely ): (i) your friend’s birthday, (ii) a holiday (e.g., New Year’s), (iii) because something reminded you of a shared experience, (iv) just because (no particular reason), (v) you were thinking about them, (vi) you heard a good joke, saw a cute picture/video, or thought of something they might enjoy, (vii) you were going to be in their neighborhood, or near their workplace, and (viii) to ask for help/advice. Participants could also type another reason for reaching out, if desired.

Several additional measures were included and are not reported in the main text. For instance, we asked participants how they would feel if they didn’t reach out, and the extent to which they and their old friend would view reaching out as an act of kindness.

A total of 266 young adults from the United Kingdom and United States, recruited on Prolific in exchange for payment, answered questions as part of a larger study. This sample size was calculated a priori to provide appropriate power for the larger study. Of those, 67 (25%) had never lost touch with someone, and were not invited to continue with the survey. This left a final sample of 199 participants ( M age = 27.4, SD = 1.9; 122 women, 73 men, 4 other; n UK = 94, n US = 58, n missing = 47).

As in Study 1, participants were asked to indicate whether they had lost touch with a friend they care about and, if so, to provide their old friend’s initials. Participants were then randomly assigned to think about either reaching out to ( n = 100) or hearing from ( n = 99) the old friend.

Using similar questions as in Study 1, participants were asked how interested they would be to reach out to [hear from] their old friend via phone, text, or email to say hello—sometime in the future and right now. Responses were provided on scales ranging from 1 = not at all , to 7 = definitely .

Once again, several additional measures were included, and are not reported in the main text. As in Study 1, we asked participants to what extent various barriers held them back from reaching out, and how willing they would be to reach out given various reasons (see the Supplementary Note, Study 2 , including Supplementary Figs. 1, 2 , for results related to these measures). We also asked participants how positive/negative they would feel if they reached out to/heard from their old friend, and how positive/negative they would feel if they/their friend wanted to reach out but decided not to. Finally, we asked participants the extent to which they consider reaching out to/hearing from their old friend as an act of kindness.

In addition to the central prediction that participants would be more interested in hearing from than reaching out to an old friend, we predicted that people would see each reason (e.g., because it’s their birthday) as better justification for hearing from vs. reaching out to an old friend. We report the results of this hypothesis in the Supplementary Note, Study 2 .

A total of 495 participants from the United Kingdom, the United States and Canada started this experiment on Prolific in exchange for payment. Of those, 28 had never lost touch with someone (i.e., they did not pass our screening question), and 14 chose not to continue with the full study when given the option after completing this screening question. This left a final sample of 453 people ( M age = 39.3, SD = 12.8; 237 women, 213 men, 3 other; n UK = 334, n US = 90, n Canada = 29). A priori calculations indicated that a sample of 432 was needed to detect a small/medium effect ( f = 0.15) with 80% power, using a between-subjects ANOVA with alpha set to .05.

At the start of the study, participants were asked to indicate whether they had lost touch with someone who (i) they would be happy to reconnect with, (ii) they had contact information for, and (iii) they thought would like to hear from them. Only participants who were able to identify a target meeting these criteria were allowed to proceed to the study, where they were asked to provide the initials for the person so that the remaining questions could be personalized.

Participants were then asked to imagine that they were going to reach out to the person they had identified, and were given 2 min to draft a “hello” message. They were told that the message could be as short or long as they wanted. Participants were informed that they could not proceed until the 2 min had passed, so they should use the time to type a short message. All participants chose to type a message, such as “Hello you. It’s been an age again. Hope you’ve been keeping well, Miss you.” Messages ranged from 3 words (“hello, hows life”) to 184 words ( M = 42.6, SD = 25.7).

Manipulation

Participants were randomly assigned to see one of three prompts encouraging them to send their note.

In the control condition (n = 149), the prompt said: “We encourage you to take this time to open an email or text message and send the message you wrote.”

In the reflective condition (n = 151), the following was added to the control prompt: “Think about how much you would appreciate it if you got a note from [your old friend]. Someone has to reach out first - why not you?”

In the impulsive condition (n = 153), the following was added to the control prompt: “If you are having second thoughts, we suggest you do not entertain them. Don’t doubt yourself - just open an email or text message, paste in your note, and press ‘send.’”

The two interventions were intended to reflect the rich history of dual processing models in psychology, which suggest that people have two thinking styles: one that is slower, more effortful, deliberate, and reflective, and another that is faster, more effortless, impulsive, and intuitive 37 , 38 , 39 , 40 . Participants in all conditions were told that they could not proceed in the survey until 1 min had passed.

Our pre-registered dependent variable was whether participants sent a message to their old friend. To capture this behaviour, we asked participants whether they sent the message, and provided three response options: yes, no, and “maybe later”, which was included to encourage honesty. We also encouraged honesty by assuring participants that their pay would not be impacted by their response. We pre-registered our intention to treat “maybe later” as “no,” because we wanted to measure actual behaviour, rather than intentions.

After deciding whether or not to send a message to their old friend, participants reported their current positive ( ɑ = 0.93) and negative emotion ( ɑ = 0.90) on the Positive and Negative Affect Schedule 41 , with “happy” added as an additional positive emotion item.

Again, several additional measures were included and are not reported in the main text. We asked participants how much they considered the following while making their decision: possible rewards (to be nice, because they miss their old friend), and a range of barriers (similar to the ones in Study 1), including worries about potential attributions that their old friend might make (that they were lonely or had an ulterior motive; see the Supplementary Note, Study 3 , including Supplementary Figs. 3 , 4 , for results related to these measures). We asked people who chose to reach out to describe their biggest motivator, and we asked people who chose not to reach out to describe their biggest barrier. Finally, we asked participants the extent to which their old friend would consider their message an act of kindness (or would have done so if they had chosen to reach out).

A total of 732 participants from the United Kingdom, United States, and Canada started this experiment on Prolific in exchange for payment. Of those, 63 had never lost touch with someone (i.e., they did not pass our screening question), 55 thought of someone who did not meet our eligibility criteria (see below), and 10 chose not to continue to the full study when given the option, after completing this screening question and thinking of someone who met all the eligibility criteria. This left a final sample of 604 people ( M age = 40.5, SD = 13.0; 274 women, 327 men, 3 other; n UK = 455, n US = 128, n Canada = 21), which surpassed our target sample of 600 participants calculated a priori to provide 90% power to detect a small/medium effect ( f = 0.15) with alpha set to 0.05.

Participants were asked to indicate whether they had lost touch with someone, using the same instructions as in Study 3, but with the addition of specifying that it should be someone they had lost touch with for no particular reason (i.e., not a falling out). To confirm eligibility, we asked people to tick a box to indicate that the person they were thinking of met each of our criteria: (i) someone who they would be happy to reconnect with, (ii) someone they had lost touch with for no particular reason, (iii) someone for whom they had contact information at hand, and (iv) someone they thought would like to hear from them. If they ticked all four boxes, they were able to continue the survey; if they did not tick even one of the boxes, they were not invited to continue to the full survey.

Participants were randomly assigned to one of three conditions. In the message condition (which was similar to the control condition in Study 3; n = 204), participants were given 2 min to compose a short message to their old friend, and were not able to proceed until 2 min had elapsed. Afterwards, participants were given 1 min to send their message. Participants saw a prompt saying, “Now we’d like to give you the opportunity to reach out to [friend’s initials]. We encourage you to take this time to open an email or text message and send a message.” The note the participant had written was shown on the screen so that participants could copy and paste it into a message if they wanted.

Participants in the message plus encouragement condition (n = 206) received the same instructions as the message condition but were additionally told: “Research suggests that sending a short message to someone to say that you are thinking of them (or hope they are well) is an act of kindness—and that this gesture is likely to be appreciated by your friend, even more than you expect. Also, a note of this sort does not suggest to your friend that you expect a response or require any further contact, so your message has low potential for risk, and high potential for reward.” We thought this intervention would (i) position the hello message as an act of kindness, to minimize concerns that it was a bother for the recipient, and (ii) reduce fears of rejection, by suggesting that participants should not expect a reply. As in the message condition, the note the participant had written could be copied and pasted into a message.

Finally, in the control condition (n = 194), participants were not given time to prepare a note, but were instead given 2 min to write about a typical day, and were not able to proceed until 2 min had elapsed. Afterward, participants saw a prompt saying: “Now we’d like to give you the opportunity to reach out to [friend’s initials]. We encourage you to take this time to open an email or text message and send a message.”

Messages written by participants in the experimental conditions ranged from 1 word (“hello”) to 129 words ( M = 44.2, SD = 21.4).

We asked participants if they sent their message to their old friend or not, using the same question used in Study 3. Again, we pre-registered our intention to treat “maybe later” as “no.” We also asked participants how much they had considered several barriers while making their decision (see Supplementary Note, Study 4 for results). We used a shorter list of barriers than in earlier studies, including only the ones that we thought might be affected by the manipulation.

A total of 303 participants were recruited in public spaces on a university campus in Canada in exchange for candy. As required by the local ethics board at the site of data collection, participants were asked before the study to provide informed consent for participation and, separately, to grant permission to share their responses in an online repository for open science initiatives. We report results from the sample of 288 participants who gave permission to share their data ( M age = 20.7, SD = 2.9; 172 women, 107 men, 5 gender fluid/non-binary/both, 4 participants with undisclosed gender), so that these findings can be replicated with the file posted on the OSF. Findings do not differ in the full sample. We pre-registered our intention to recruit at least 275 participants to provide 90% power to detect a small size effect ( d = 0.2) with a paired samples t-test and alpha at 0.05.

Participants completed a short online survey in which they were asked to rate their willingness to engage in various common activities right away. To increase the believability that participants may be asked to complete a task immediately, we kept props for some actions nearby, including a cooler bag to hold ice cream bars, bags of coins, a hand grip, and a large garbage bag for trash collection.

Participants rated their willingness to complete eight everyday activities right now on a scale ranging from 1 = extremely unwilling to 7 = extremely willing : (i) call or text an old friend that you have lost touch with, (ii) talk to a stranger, (iii) listen to a song you loved in your childhood or teen years, (iv) eat an ice cream bar, (v) sort a bag of coins, (vi) hold a hand grip for 30 s, (vii) book a dentist appointment or physical exam, and (viii) pick up litter. Items were presented in random order.

Participants were asked whether they had lost touch with a friend they care about (yes/no), and whether they had ever thought about reaching out but did not (yes/no). See Supplementary Note, Study 5 for detailed results on these exploratory measures.

A total of 505 participants were recruited from the United Kingdom, the United States and Canada on Prolific in exchange for payment. They completed a pre-screening survey to see if they could identify three to five people they “haven’t been in touch with for a while.” Of these, 502 were able to do so, and were invited to complete the full survey, though we limited participation to 320 people. Of the 324 participants who completed the full survey, 319 ( M age = 39.5, SD = 13.4; 138 women, 176 men, 5 other; n UK = 171, n US = 118, n Canada = 30) passed our pre-registered attention check and form our final sample.

Given the challenges involved in power analysis for mixed models, we based our power analysis on a between-subjects design, which should be more conservative. Our power analysis suggested that, in order to have 80% power to detect a small sized bivariate correlation ( r = 0.15) with an alpha of 0.05, we needed 273 participants, so we pre-registered a recruitment target of 300 participants.

Participants completed a short online survey in which they named three to five people they had not been in touch with for a while (“old friends”; n = 121 people named three old friends, n = 55 named four, and n = 143 named five), and answered a few questions about each old friend, including familiarity, and willingness to reach out. For exploratory purposes, participants also named a current friend (someone they “know fairly well and have recently been in touch with”) and a new acquaintance (“someone [they] recently met and interacted with for the first time”), and answered the same questions about these targets (see Supplementary Note, Study 6 for analyses involving these targets).

Participants indicated the type of relationship they had with each target, by ticking all that apply from a list of seven options (or “other”).

Participants reported how recently they had been in touch with each target, on a 5-point scale from 1 = more than a few months ago to 5 = in the last few days . Critically, participants rated how well they currently know each target, on a 7-point scale from 1 = I know them as well as a stranger to 7 = I know them as well as I know myself . Finally, participants rated their willingness to reach out right now (via phone, text, email, social media, or in-person) to say hello to each target, on a 7-point scale from 1 = very unwilling to 7 = very willing .

A total of 348 people were recruited in person on a university campus in the U.K. and were reimbursed with chocolate and a chance of winning a draw prize. Of these, 237 were eligible to complete the survey, because they were able to think of someone they had lost touch with who met all of our criteria - the same criteria used in Study 4, which we verified using tick boxes, as in Study 4. We excluded two additional participants because they were taking a class taught by one of the authors, in which some of the studies in the current paper had been discussed. Our final sample consisted of 194 participants ( M age = 23.2, SD = 7.5; 112 women, 65 men, 10 other ways, and 7 participants with undisclosed gender) who answered the key question about whether or not they had reached out to their old friend. This final sample surpasses our pre-registered target sample of 160 participants needed to provide 80% power to detect a medium size effect ( dz = 0.4) with an independent samples t-test and a one-tailed alpha of 0.05.

Participants completed an online survey in which they thought of someone they had lost touch with. They were randomly assigned to either the practice ( n = 101) or no-practice (i.e., control; n = 93) condition, in each of which they completed a task for 3 min. Participants in the practice condition were asked to “send messages (via text, chat, etc.) to several current friends/acquaintances”, and on average they sent messages to about three people ( M = 3.3, SD = 1.8). Participants in the no-practice condition were asked to “browse several social media accounts/feeds”, and on average they browsed six or seven accounts ( M = 6.7, SD = 14.5). Participants were not able to continue to the next page of the survey until 3 min had elapsed. Next, participants in both conditions were encouraged to send a message to their old friend, and were told that it was an act of kindness that would benefit them (increase their happiness), and would be appreciated by their friend. Participants were not able to continue to the next page of the survey until 2 min had elapsed. Participants reported whether or not they had sent the message, then answered some questions about their emotions, and the barriers and motivators that they had considered when deciding whether or not to send their message.

Our pre-registered dependent variable was whether participants sent a message to their old friend, which we assessed the same way as in Studies 3 and 4. Participants also reported their current positive ( ɑ = 0.87) and negative emotion ( ɑ = 0.82), on the same scale as in Study 3.

We also asked participants several additional questions that are not reported in the main text, such as how much they considered the following while making their decision: a range of barriers, including worries about potential attributions that the target might make ( ɑ = 0.77), and motivations ( ɑ = 0.80), on the same measures as in Study 3 (see Supplementary Note, Study 7 for results related to these measures). We asked participants to describe the type of relationship they had with their old friend (81% were/had been close friends), how they knew their old friend (76% knew them from school), and how recently they had been in touch with their old friend ( M = 1.6, SD = 0.9), using the same measures as in Study 6. We asked participants who had chosen to reach out to their old friend how glad they were to have sent the message ( M = 3.8, SD = 0.8), and how glad they thought the recipient would be to have received the message ( M = 3.5, SD = 1.0). Finally, we asked participants in the practice condition how many of their current friends/acquaintances that they had sent messages to during their practice session had responded before they decided whether or not to reach out to their old friend ( M = 1.1, SD = 1.5).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

A chi-square analysis revealed that a significant majority (90.9%) of participants had lost touch with a friend they care about, X 2 (1) = 295.5, p < 0.001. Yet, participants did not report being particularly willing to reach out to their old friend in the future, as evidenced by ratings ( M = 4.1, SD = 1.9) that did not differ from the midpoint of the scale labeled as “neutral/undecided” according to a Bayesian one-sample test (assuming a diffuse distribution for priors on the variance and mean, and using a Monte Carlo approximation based on 10,000 samples), BF01 = 22.18 (strong evidence in favour of the null hypothesis), t (400) = 0.52, p = 0.60, d = 0.03, Δ M = 0.05, CI 95 = [−0.14, 0.24]. Participants were even less willing to reach out to this same target right now ( M = 3.3, SD = 2.0), with responses to this question falling significantly below the midpoint of the scale, t (399) = −7.33, p < 0.001, d = −0.37, Δ M = −0.74, CI 95 = [−0.94, −0.54]. Both of these results hold after applying a Bonferroni correction for multiple comparisons. The hesitation to reach out is perplexing given that participants expected their friend to view them ( M = 4.3, SD = 1.4) and their message ( M = 4.4, SD = 1.4) positively (i.e., above the neutral scale midpoint), t (400) = 4.79, two-tailed p < 0.001, d = .24, Δ M = 0.34, CI 95 = [0.20, 0.48], and t (400) = 5.82, two-tailed p < 0.001, d = 0.29, Δ M = 0.40, CI 95 = [0.26, 0.53], respectively.

People indicated that a variety of barriers hold them back from reaching out (see Fig. 1 ). The most strongly endorsed barrier was a concern that the friend may not want to hear from them ( M = 5.2, SD = 2.1), followed by a concern that it may be awkward to reach out after all this time ( M = 4.9, SD = 2.2), both of which were endorsed above the midpoint of the scale using separate one-sample, non-directional t-tests, t (400) = 11.05, p < 0.001, d = 0.55, Δ M = 1.16, CI 95 = [0.95, 1.37], and t (400) = 7.63, p < 0.001, d = 0.38, Δ M = 0.85, CI 95 = [0.63, 1.06], respectively. Meanwhile, participants reported that only a few situations offered a legitimate reason for reaching out to their old friend. The most compelling reason for reaching out was their friend’s birthday ( M = 4.8, SD = 2.1), which was rated as significantly higher than the midpoint of the scale using a one-sample, non-directional t-test, t (397) = 7.16, p < 0.001, d = 0.36, Δ M = 0.77, CI 95 = [0.56, 0.98] (see Fig. 2 ).

Boxplot showing all the data; barring missing data, all participants ( N = 401) rated all items. The upper and lower hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the line in the boxplot, and the mean is indicated by the blue diamond.

Study 1 revealed that the majority of people have lost touch with a friend they care about, but report neutral feelings, at best, about reaching out to their old friend. Further, people acknowledge that a wide range of barriers prevent them from reaching out and few reasons warrant them reaching out. These hesitations are notable in light of participants reporting that they expect themselves and their message to be well-received.

Does a reluctance to reach out to old friends stem from a hesitation to reconnect or a hesitation to initiate contact? Recent research suggests that people are particularly anxious about initiating conversations 36 , so in Study 2 we examined whether one’s willingness to reconnect differs depending on one’s role in the exchange. Specifically, we predicted that people would be more willing to reconnect if their old friend initiated contact than if they were the one having to initiate.

Consistent with our pre-registered hypotheses, two independent samples t-tests found that participants were more interested in hearing from a friend, both now ( M = 4.9, SD = 2.0) and in the future ( M = 5.4, SD = 1.7), than reaching out to a friend ( M now = 3.5, SD = 1.9; M future = 4.8, SD = 1.9; t (197) = 4.93, one-tailed p < 0.001, d = 0.70, Δ M = 1.36, CI 95 = [0.82, 1.90], and t (197) = 2.49, one-tailed p = 0.01, d = 0.35, Δ M = 0.63, CI 95 = [0.13, 1.14], respectively, see Fig. 3 ). This suggests that initiating contact may be a primary challenge to reconnecting.

Boxplot showing all the data; barring missing data, all participants ( N = 199) rated their interest at both time points. The upper and lower hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the line in the boxplot, and the mean is indicated by the blue diamond.

Studies 1 and 2 demonstrate that people are surprisingly unwilling to reach out to an old friend, but self-reported responses may fail to capture how people actually behave. Therefore, in Study 3, we examined actual behaviour.

Across conditions, fewer than a third of participants (27.8%) reached out by sending a message to their old friend. We used a one-way ANOVA to test our pre-registered hypothesis that the proportion of people who sent their message would differ across conditions. Counter to predictions, we did not find evidence that reaching out rates differed across conditions, F (2, 450) = 1.72, p = 0.18, \({\eta }_{p}^{2}\) = 0.01, CI 95 = [0.001, 0.03], with 27.5% sending the message in the control condition, 23.2% in the reflective condition, and 32.7% in the impulsive condition. Bayesian independent t-tests assuming unequal variance and using diffuse priors found moderate evidence in favour of the null hypothesis: t (298) = 0.86, p = 0.39, BF01 = 7.65 for reflective vs. control, and t (300) = 0.98, p = 0.33, BF01 = 6.93 for impulsive vs. control. Thus, we did not find evidence that encouraging people to adopt a reflective or impulsive thinking style increased the likelihood that they would reach out to an old friend.

Exploratory analyses indicated that participants who sent a message to their old friend reported more positive emotion ( M = 3.3, SD = 0.8) and less negative emotion ( M = 1.5, SD = 0.6) afterward than people who did not reach out ( M PA = 2.6, SD = 0.9; M NA = 1.7, SD = 0.7), t (451) = −7.22, two-tailed p < 0.001, d = 0.76, Δ M = −0.64, CI 95 = [−0.82, −0.47], and t (451) = 2.96, two-tailed p = 0.002, d = 0.31, Δ M = 0.22, CI 95 = [0.07, 0.36], respectively. While these data are consistent with the idea that reaching out to an old friend is emotionally rewarding, the present data are correlational in nature and therefore cannot rule out the possibility that people experiencing greater positive emotions and lower negative emotions were more willing to reach out.

In Study 3, fewer than one third of people took the opportunity to reach out to an old friend, even though they wanted to reconnect with the target, thought the target wanted to hear from them, had the target’s contact information, and were given time to draft and send a message. These findings converge with the self-reports from Studies 1–2 to further demonstrate that most people are reluctant to reach out to an old friend. In addition, the two interventions designed to encourage reaching out, by changing people’s thinking about the act, were unsuccessful.

We wondered whether the low and relatively stable levels of reaching out in Study 3 may have been a result of the study design, therefore we made two changes in Study 4. First, we took a bottom-up approach to designing the intervention, targeting the particular barriers that participants endorsed in Studies 1–2 when thinking about reaching out to old friends. Additionally, it is possible that we did not detect differences across conditions in Study 3 because the control condition elevated reaching out rates by providing participants with time to write a message. Therefore, in Study 4, we designed a more realistic control condition.

We predicted that participants in both the message and message plus encouragement conditions would be more likely to reach out than participants in the control condition, and that people in the message plus encouragement condition would be more likely to reach out than participants in the message condition.

Across conditions, just over one third of participants (36.8%) reached out to a friend. Counter to predictions, a one-way ANOVA did not show evidence of different reaching out rates across conditions, F (2, 601) = 1.93, p = 0.15, \({\eta }_{p}^{2}\) = 0.01, CI 95 = [0.001, 0.02], with 42.3% of participants sending their message in the control condition, 33.3% in the message condition, and 35.0% in the message plus encouragement condition. Follow-up paired comparisons using a Tukey’s test did not reveal any differences between the message and control conditions, p = 0.16, Δ M = 0.09, CI 95 = [−0.02, 0.20], between the message plus encouragement and control conditions, p = 0.28, Δ M = 0.07, CI 95 = [−0.04, 0.19], or between the two experimental conditions, p = 0.94, Δ M = −0.02, CI 95 = [−0.13, 0.10]. Similarly, Bayesian independent t-tests assuming unequal variance and using diffuse priors found some evidence in favour of the null hypothesis for message vs. control, t (396) = −1.84, p = 0.07, BF01 = 2.40 (anecdotal evidence), and for message plus encouragement vs. control, t (398) = −1.50, p = 0.13, BF01 = 4.18 (moderate evidence). Thus, we did not find evidence to suggest that addressing people’s concerns about reaching out increased the likelihood of reaching out to an old friend, and if anything, the interventions nudged participants in the opposite direction.

Studies 1–4 reveal that people both report and demonstrate a reluctance to reach out to old friends despite various forms of encouragement and the removal of several commonly cited barriers. This hesitation is problematic given that reaching out to old friends offers one meaningful route to social connection and, in turn, greater well-being. Where does this reluctance come from? Why are people unwilling to reach out to someone who they were once close to? One possibility is that old friends feel a lot like strangers, and therefore reaching out to an old friend might activate the same apprehensions that people have about talking to strangers.

A growing body of research demonstrates that people are unwilling to talk to strangers and avoid opportunities to do so. Indeed, despite several studies demonstrating that brief conversations with strangers can promote one’s happiness and belonging 29 , 30 , people report both avoiding and dreading these conversations due to a number of fears. For instance, people worry that they will not enjoy the conversation, not like their partner, and not have the necessary conversational skills (e.g., know how to start and maintain the conversation) 42 . In addition, people fear that their partner will not like them or enjoy the conversation 42 . Some of these common fears seem less relevant for old friends; people already know that they like the other person and presumably would only consider reaching out if they expected to enjoy the conversation. Indeed, in the present studies we specifically asked people to nominate an old friend that they would be happy to reconnect with. Yet, other fears seem more relevant. When reaching out to an old friend, people might worry that, even though they have interacted with the friend before, they will not know what to say after all this time, that their old friend may not be interested in hearing from them, and that the exchange will be awkward. Indeed, all of these concerns were endorsed to some degree in Study 1. Therefore, it seems plausible that people may harbour some of the same fears about reaching out to an old friend that they do when initiating a conversation with a stranger.

We explored the idea that old friends can feel like strangers in three remaining studies. Specifically, in Study 5 we examined the relative strength of people’s reluctance to reach out to old friends by benchmarking the willingness to reconnect with an old friend against the willingness to talk to a stranger: an active, social, and commonly avoided behaviour 27 , 29 . Then, in Study 6 we examined whether people are more reluctant to reach out to old friends when old friends feel more like strangers (i.e., whether familiarity acts as a mechanism). Finally, in Study 7, we applied lessons from one intervention shown to lastingly ease anxieties about talking to strangers. By assigning some participants to complete a warm-up activity, we effectively encouraged more people to reach out to old friends.

Participants’ willingness ratings are shown in Fig. 4 . On average, willingness to reach out to an old friend was lower than all but two of the seven other actions (book a medical appointment and sort a bag of coins), though the differences were not always statistically significant. Critically, as predicted, a Bayesian independent t-test assuming unequal variance, and using diffuse priors revealed that participants were no more willing to reach out to an old friend ( M = 4.6, SD = 1.7) than they were to talk to a stranger ( M = 4.6, SD = 1.7), t (287) = −0.42, p = 0.67, d = 0.03, BF01 = 19.52 (strong evidence in favour of the null hypothesis).

Boxplot showing all the data; barring missing data, all participants ( N = 288) rated all items. The upper and lower hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the line in the boxplot, and the mean is indicated by the blue diamond.

Again demonstrating people’s reluctance to reach out to old friends, Study 5 revealed that people were no more willing to reach out to an old friend than they were to perform seemingly aversive activities, such as picking up litter or holding a handgrip for 30 s. Most notably, people were no more willing to reach out to an old friend than talk to a stranger, which raises an interesting possibility: people may be reluctant to reach out to old friends because they feel like strangers. In other words, one potential reason why people are unwilling to reach out to old friends is because old friends feel unfamiliar, like strangers. Therefore, we next examined whether people are less likely to reach out to old friends that feel more like strangers (and, conversely, more likely to reach out to old friends that feel more familiar), using a within-subjects design similar to past work 32 .

Responses provided useful descriptive insight into the nature of old friendships. Specifically, participants indicated that old friends reflected various relationship types, including people who were, or had been, close friends (46%), social acquaintances (16%), family members (14%), and colleagues (13%; see Supplementary Fig. 5 in the SI for full descriptives). Old friends were primarily people that the participants knew from school (29%), through friends/family (23%) or from work (22%; see Supplementary Fig. 6 in the SI for full descriptives). Most participants reported that they were last in touch with their old friend more than a few months ago ( M = 1.8, SD = 1.1, Mode = 1), but 43% of participants had been in touch more recently.

Our primary, pre-registered hypothesis was that people would be less willing to reach out to old friends who feel less familiar. To test this hypothesis, we ran a linear mixed model using the lmer package in R 43 , examining whether lower feelings of familiarity with an old friend predicted a lower willingness to reach out, with participant id entered as a random effect. As hypothesized, familiarity predicted willingness to reach out, b = 0.63, SD = 0.03, 95% CI = [0.58, 0.68], t = 23.89, indicating that people were less likely to reach out to old friends who felt less familiar (see Fig. 5 ). Of note, familiarity was also a significant predictor of reaching out to one’s current friends, r (319) = 0.51, p < 0.001, and new acquaintances, r (318) = 0.57, p < 0.001.

Boxplot showing all the data. Participants nominated 3 to 5 old friends who varied in familiarity: 1 = I know them as well as a stranger ( N = 135), 2 ( N = 215), 3 ( N = 232), 4 ( N = 254), 5 ( N = 267), 6 ( N = 150), 7 = I know them as well as I know myself ( N = 45). The upper and lower hinges of the boxplot correspond to the first and third quartiles (the 25th and 75th percentiles). The median is indicated by the line in the boxplot, and the mean is indicated by the blue diamond.

Taken together, these results demonstrate that feelings of unfamiliarity toward an old friend predict a lower willingness to reach out. If reaching out to old friends can feel like talking to a stranger, can an intervention that reduces worries about talking to strangers encourage people to reach out?

Empirical evidence for the various benefits of talking to strangers is accumulating 29 , 30 , 44 . As a result, researchers have tested several strategies for encouraging people to talk to strangers more often. However, studies that have attempted to do so by reducing the fears that people have about talking to strangers have generally been unsuccessful 42 . One intervention, however, has been shown to lastingly change people’s attitudes about talking to strangers. This intervention involves participants playing a scavenger hunt game in which they complete a “mission” every day for a week: talking to a stranger in the experimental condition, or observing a stranger in the control condition 45 . At the end of the week, participants in the experimental condition were less worried about rejection, and more confident in their ability to start and maintain a conversation. These changes in attitude persisted for at least a week after the intervention had ended. Importantly, this study found preliminary evidence that these changes in attitude might lead people to initiate more conversations with strangers.

Given the relative success of this design, we adapted it to our purposes here by asking participants in the experimental condition to complete a warm-up task in which they sent practice messages to current friends and acquaintances. Meanwhile, participants in the control condition simply browsed social media: a similarly social, but more passive activity. We predicted that giving participants the opportunity to practice a form of the desired behaviour would encourage more people to reach out to old friends.

Consistent with our pre-registered prediction, more participants in the practice condition reached out to their old friend (53%) than did participants in the no-practice condition (31%), t (194) = 3.20, one-tailed p < 0.001, d = −0.46, Δ M = −0.22, CI 95 = [−0.36, −0.09]. Notably, the proportion of participants who reached out to an old friend in the control condition was (descriptively) similar to the proportions who reached out (across conditions) in our previous intervention studies: 27.8% in Study 3, and 36.8% in Study 4.

As in Study 3, people who reached out to their old friend reported more positive emotions ( M = 3.0, SD = .7) than people who did not reach out ( M = 2.6, SD = 0.7), t (187) = 4.45, one-tailed p < 0.001, d = −0.65, Δ M = −0.46, CI 95 = [−0.66, −0.26], but unlike in Study 3, they did not differ in negative emotions, t (187) = 1.26, one-tailed p = 0.10, d = 0.19, Δ M = 0.12, CI 95 = [−0.07, 0.30].

When friendships fade, are people eager and motivated to reach out and reconnect with old friends? Seven studies suggest that they are not. In Study 1, we saw that, although losing touch with a friend is an extremely common experience, most people express neutral or negative feelings about the prospect of reaching out to reconnect, citing several barriers and few reasons to do so. In Study 2, people were more willing to hear from vs. reach out to an old friend, which is consistent with the idea that people are especially hesitant about initiating contact, not about reconnecting. In Studies 3 and 4, we provided people with an opportunity to actually reach out to an old friend, and mitigated or removed several commonly cited barriers. Despite these aids, fewer than half of participants chose to reach out. Moreover, rates of reaching out were not meaningfully altered by a top-down manipulation informed by past research on dual processing models of human cognition (Study 3), nor a bottom-up manipulation that pre-emptively addressed common concerns (Study 4), indicating that this tendency may be difficult to change.

After observing that people endorse similar fears when thinking about reaching out to an old friend as they do when thinking about talking to a stranger, we reasoned that one explanation for why people may avoid reaching out to old friends is that old friends feel like strangers after time has passed. To explore this possibility, in Study 5, we asked participants to rate their willingness to engage in several common daily tasks. We found that participants were no more willing to reach out to an old friend than they were to talk to a stranger. Moreover, in Study 6, we found that people were more reluctant to reach out to old friends when those friends felt more like strangers. Therefore, in Study 7, we adapted an intervention shown to ease anxieties about talking to strangers, which effectively increased by two-thirds the number of people who chose to reach out to an old friend.

The current findings add to the mounting body of research demonstrating that people undervalue social activities and actions 25 . Critically, this work also offers a number of extensions. First, we examine behaviour rather than (mis)predictions of how one thinks they would behave or how they expect themselves or others to feel. Indeed, Studies 3, 4, and 7 examine what proportion of participants actually reach out to old friends, which moves the literature beyond self-reports, expectations and misestimations, towards action 46 . Second, we document a reluctance to reach out to old friends in a range of relevant social contexts, such as being reminded of a shared memory or an upcoming holiday (Study 1), and in the face of several interventions (Studies 3-4). Thus, these data illustrate the pervasive nature of the reluctance to reach out. Finally, in Study 7, we provide evidence for an intervention that effectively increases reaching out to old friends - a behaviour that has informational and well-being benefits.

The intervention used in Study 7 to boost reaching out rates focused on changing peoples’ behaviour by having them practice a version of the desired task. This intervention parallels the most successful strategy detected to date to encourage people to talk to strangers—simply practicing the task – and is a notable departure from most past research, which has tried and failed to promote social behaviour by educating or convincing people of the benefits of such actions 28 , 42 . Therefore, these findings align with recent theorizing on the potential benefits of targeting interventions toward the social context or situation, and away from altering attitudes because the latter may be slower or more resistant to change 47 .

Of course, this does not mean that peoples’ attitudes and appreciation of the benefits of reaching out have no impact. Data from Study 1 revealed that the more participants thought their friend would appreciate them reaching out, the more willing they were to reach out to their friend now and in the future. Along similar lines, participants in Study 1 who saw reaching out as more of a prosocial act were more willing to engage in the behaviour, both now and in the future. These findings suggest that interventions designed to change peoples’ minds or attitudes – by proactively signaling the recipient’s appreciation or framing reaching out as an act of kindness—may ultimately be successful 28 . However, it is possible that these interventions must be more explicit or intensive to be effective because, by targeting attitudes, they are one step further removed from the behaviour they aim to change.

Reconnecting with old friends may bring opportunities for social connection and greater well-being, but this only happens if at least one party is willing to reach out. The present data suggest that people are generally interested in connecting, but prefer that the other person initiate (see Study 2). These findings align with previous work that finds that people are more interested in hearing personal information about others than they are in sharing similar information about themselves 48 . Is the hesitation to initiate because people assume that others are more likely to reach out than they truly are? In the SI, we report one study (Supplementary Note, Study S 8 ) demonstrating that people overestimate the willingness of others to reach out. Specifically, participants read about the control condition in Study 3 and were asked to predict what percentage of participants would send a message to an old friend. Participants estimated that 56.6% would reach out, which was nearly double the actual percentage observed (27.5%). These data are consistent with the possibility that people think others will reach out, thereby relieving them of the task, and could be explored more deeply in the future. Indeed, Supplementary Note Study S 9 in the SI demonstrates that people also overestimate their own willingness to reach out to old friends. Thus, people may hold various flawed assumptions about reaching out.

Limitations

The present work has some limitations that can be considered in future research. First, the seven studies presented here considered reaching out to an old friend that participants wanted to reconnect with. Not all estranged friendships lapse from neglect; some friendships end on painful or angry terms, offering clear reason for disengagement. We focused on the former context both because we suspected this situation to be common, and because we thought it would provide a generous assessment of reaching out intentions and behaviour. Future researchers could consider how to encourage reaching out, if desirable, in more complicated relational contexts, such as when one or both parties are not eager.

Second, our studies collected data from participants in Western countries and the findings may therefore not generalize to other countries and contexts. Research on relational mobility suggests that in some contexts it is adaptive to have a wide network of weaker relationships, whereas in other contexts it is adaptive to maintain a smaller network of close relationships 49 . Future work could therefore expand this investigation to other cultural and socioeconomic contexts, which may differ in the extent to which they allow relationships to lapse, and value reconnecting when they do.

Finally, despite several studies examining people’s willingness to reach out to an old friend and a stranger, we did not directly compare the experiences of these two actions. In light of past research and the present findings, we hypothesize that both experiences would be more positive than people expect. However, it is unclear which act would lead to greater momentary well-being. It seems plausible that reaching out to an old friend may promote greater happiness (than talking to a stranger) if the old friend responds quickly and positively, thus signaling mutual care in a way that is difficult to experience with strangers. This fascinating comparison remains an open question for future research.

Implications

Western societies are growing increasingly concerned about loneliness and its dire impact on physical as well as psychological well-being 50 . Loneliness stems from perceiving fewer or lower quality social connections than one desires 51 . As a result, one intuitive idea about how to reduce loneliness is to help people build new social connections. However, building new social connections is difficult: it requires opportunities to meet new people, the social skills to initiate conversations with new people (i.e., strangers), not to mention repeated interaction and time spent together 22 . Alternatively, it might be easier and more efficient for people to revive existing relationships. Indeed, the empirically-informed Groups4Health program recommends just that 24 . However, the current research suggests that this recommendation may come with significant and previously unacknowledged challenges. The present findings suggest that more work is needed to understand how to break down the barriers, and support people in reaching out to reconnect.

Similarly, the current research suggests that a re-examination may be in order for one common positive psychology intervention for increasing well-being: practicing gratitude. People are often encouraged to write and send or deliver a thank you message to someone that they have not properly thanked. We suspect that, in practice, people often choose to thank someone who they have lost touch with: a favourite teacher from their school days or a workplace mentor from the early days of one’s career. If this is the case, then people’s predictions about what it will feel like to send the gratitude letter, and their decisions about whether or not to actually send the letter, are likely more complicated than formerly recognized; expressing gratitude may be confounded with reaching out to someone they have lost touch with. As a result, people may forgo opportunities to express gratitude, and ultimately experience greater happiness. Given the conceptual and practical overlap between reaching out and expressing gratitude, we hope researchers will investigate ways to help people overcome their hesitations to reach out, thereby making other happiness-boosting activities more likely as well.

Decades of research from across the social sciences indicates that relationships provide one of the most direct routes to happiness 1 , 2 , 52 . While recent years have expanded this examination to include brief interactions with strangers and acquaintances 29 , 30 , the present work offers a timely and valuable reminder of one potentially overlooked source of social connection—reaching out to old friends. Indeed, we find that reaching out may also provide emotional benefits; participants in two of the present studies reported greater well-being after sending a message to an old friend than participants who opted not to do so (Studies 3 and 7). While the current data are correlational and should therefore be interpreted with caution, the observation that participants are happier after a social act is consistent with a large body of research demonstrating the hedonic rewards of brief social interactions and socialization 36 , 48 , 53 . Therefore, reaching out to old friends may offer an additional channel to social connection, and in turn, greater well-being.

Relationships can fade for a variety of reasons. The present work demonstrates that the majority of people are reluctant to reach out to old friends, even when they are personally interested in doing so, believe their friend wants to hear from them, and are provided with time to draft and send a hello message. Moreover, this reluctance may be stubborn and difficult to change. One reason for this reluctance may be that old friends feel like strangers. Supporting this possibility, we find that people are no more willing to reach out to an old friend than they are to talk to a stranger, and that people are less willing to reach out to old friends who feel less familiar—more like strangers. Fortunately, one study reveals that people are more willing to reach out to an old friend after they practice the behaviour. More research is needed to understand how best to encourage people to reach out, so that they can experience the health and happiness benefits that come with increased social connection.

Data availability

All materials and data are available on the Open Science Framework (OSF): https://osf.io/kydb3/ .

Code availability

Data were analyzed using SPSS 28.01 and R version 4.3.2. All code for analyses is available on the OSF: https://osf.io/kydb3/ .

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Acknowledgements

We thank Marcel Aini, Anurada Amarasekera, Gurleen Bath, Lily Buttery, Kristina Castaneto, Dani Conception, Jaymie Cristobal, Katrina Del Villar, Fiona Eaket, Angie Fan, Amanda Hodges, Ravneet Hothi, Elyssa Hutchinson, Tori Kazemir, Allyson Klassen, Kalum Kumar, Erin Koch, Jacob Lauzon, Yassaman Malekzadeh, Katy Rogers, Marwan Saleh, Mia Sherley-Dale, Emily Stern, Naimah Sultana, Kelton Travis, Sophia Vennesland, and Rachael Whyte for their help with data collection, and Janaki Patel for her invaluable assistance with numerous tasks. The authors received no specific funding for this work.

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Lara B. Aknin developed the study concept, contributed to the study design, collected the data, analyzed the data, and drafted the manuscript. Gillian M. Sandstrom developed the study concept, contributed to the study design, collected the data, analyzed the data, and drafted the manuscript.

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Aknin, L.B., Sandstrom, G.M. People are surprisingly hesitant to reach out to old friends. Commun Psychol 2 , 34 (2024). https://doi.org/10.1038/s44271-024-00075-8

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BRIEF RESEARCH REPORT article

The impact of benevolent childhood experiences on adult flourishing: the mediating role of light triad traits.

1 School of Psychological Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras
2 Faculty of Education and Sport Sciences, University of Granada, Melilla, Spain

The literature has well documented the relationship between Adverse Childhood Experiences, personality traits, and well-being. However, less is known about how Benevolent Childhood Experiences (BCEs) relate to “light” personality traits and Flourishing. The study analyzed the effects of BCEs on Flourishing, considering the mediator role of Light Triad traits (Kantianism, Humanism, and Faith in Humanity). The study used a quantitative methodology with a non-experimental, cross-sectional design; 410 Honduran adults responded to the survey, including questions regarding Light Triad personality traits, Flourishing, and BCEs. On average, respondents reported 7.34 BCEs. The number of reported BCEs did not vary significantly between men and women. However, specific BCEs were categorically associated with subjects’ sex. A higher proportion of men reported having at least one teacher who cared about the respondent, having opportunities to have a good time, and liking/feeling comfortable with oneself. Flourishing was significantly higher for participants who reported the presence of BCEs. The largest effect size was achieved for the difference in Flourishing scores between those who reported liking school as a child and those who disliked it. The number of Benevolent Childhood Experiences had a significant total and direct effect on Flourishing scores. Significant indirect effects were also identified. Faith in Humanity and Humanism, not Kantianism, mediated the relationship between BCEs and Flourishing. BCEs significantly explained all Light Triad traits. In conclusion, BCEs have significant direct and indirect effects on adult Flourishing; Faith in Humanity and Humanism mediate this relationship.

1 Introduction

Mental health encompasses well-being, enabling individuals to confront vital challenges and fulfill their potential. The concept extends beyond the absence of psychopathology ( WHO, 2022 ), as it also englobes human strengths, positive emotions, and subjective well-being ( Vaillant, 2012 ); this is often referred to as positive mental health. Psychotherapists consider positive mental health screening and interventions innovative and valuable assets for the therapeutic process ( Chang et al., 2022 ).

Childhood experiences have diverse outcomes in various adulthood domains, including physical and mental health and family well-being ( Crandall et al., 2019 ; Daines et al., 2021 ; Mosley-Johnson et al., 2021 ). These childhood experiences can be either benevolent (positive) or adverse (negative). Research has shown how Adverse Childhood Experiences (ACEs) are associated with adult personality traits. Specifically, ACEs are negatively related to conscientiousness and positively associated with neuroticism ( Grusnick et al., 2020 ). Such experiences are also positively related to “dark” traits, including psychopathy, borderline personality disorder, and narcissism ( Wilson et al., 2023 ), as well as lower subjective well-being ( Wang et al., 2022 ). Therefore, there is clear evidence of the relationship between Adverse Childhood Experiences, “dark” personality traits, and well-being. However, less is known about how Benevolent Childhood Experiences (BCEs) relate to “light” personality traits and Flourishing.

Benevolent Childhood Experiences include the social and family support a person receives before turning 18; they also involve comfortable beliefs, opportunities for having a good time, stable home routines, self-acceptance, and school enjoyment ( Narayan et al., 2018 ). Recent studies suggest that BCEs predict lower symptoms of depression, stress, and loneliness during adulthood. It is worth noting that BCEs are significant mental health promoters independent of ACEs ( Doom et al., 2021 ).

The Light Triad (LT) of personality focuses on positive traits: Humanism, Faith in Humanity, and Kantianism. Humanism refers to the belief that people are inherently worthy and have dignity. Faith in Humanity is a predisposition to focus on the best in people and to believe that most people are good. Kantianism is the belief that people are ends unto themselves. Previous studies suggest that such traits are positively related to life satisfaction ( Kaufman et al., 2019 ).

On the other hand, Flourishing refers to a multidimensional state of optimal psychological well-being characterized by positive emotions and relationships, engagement in activities, a sense of meaning and purpose, and a sense of accomplishment and personal growth. It represents a holistic and positive perspective on well-being, focusing on cultivating and enhancing individuals’ positive functioning and overall quality of life. Flourishing extends beyond the absence of negative symptoms or disorders, emphasizing promoting positive attributes and experiences ( Diener et al., 2010 ). As such, it is considered an essential aspect of mental health promotion ( Burns et al., 2022 ). Previous studies have shown that experiencing parental warmth during childhood is significantly associated with Flourishing scores during mid-adulthood ( Chen et al., 2019 ); suggesting a link between childhood experiences and adulthood subjective well-being ( Yu et al., 2022 ).

Studying BCEs in a specific cultural context, such as Honduras, enhances our understanding of how cultural factors shape childhood experiences and their impact on adulthood. Cultural values, norms, and practices play a crucial role in shaping the experiences and perceptions of individuals. By examining BCEs within the Honduran context, researchers can identify culturally specific factors contributing to positive childhood experiences and their potential long-term effects on individuals’ well-being. Research conducted in diverse cultural contexts helps us move beyond a narrow focus on Western perspectives and provides a more comprehensive understanding of the universal and culturally specific factors contributing to positive childhood development.

In this sense, Honduras has been considered one of the most violent countries in the world. Young people are at high risk of being victims of violence ( Landa-Blanco et al., 2020 ). Poverty and illegal migration are also prevalent in the country. Many children suffer traumatic experiences before, during, and after the migratory process; it is common for children to migrate unaccompanied ( Linton et al., 2018 ). Most young adults sampled in a national study have experienced ACEs ( Huber-Krum et al., 2022 ). Women were at a higher risk of reporting ACEs related to sexual, emotional, or physical violence. Experiencing ACEs was related to a greater prevalence of depression, distress, and suicide risk ( Kappel et al., 2021 ). Recent studies from Honduras have also reported that certain ACEs exhibit notably detrimental effects on mental health outcomes. These include instances of coerced sexual activity, exposure to domestic violence within the family, verbal degradation, and residing with individuals grappling with mental health challenges, substance abuse problems, or incarceration histories ( Landa-Blanco et al., 2024 ).

While these studies offer valuable insights, they tend to adopt a psychopathology-focused approach, overlooking key dimensions of the well-being spectrum. Therefore, from a positive mental health approach, the current study analyzed how Benevolent Childhood Experiences affect Flourishing, considering the mediator role of Light Triad personality traits in the Honduran population. Taking into account the literature review presented here, the following hypotheses were established:

• Hypothesis 1 : Benevolent Childhood Experiences have positive direct effects on Light Triad traits.

• Hypothesis 2 : Benevolent Childhood Experiences have positive direct effects on Flourishing.

• Hypothesis 3 : Benevolent Childhood Experiences have positive indirect effects on Flourishing, Light Triad traits mediate this relationship.

2.1 Participants

A total of 410 participants responded to the survey; 255 (62%) were women, and 155 (38%) were men. Their average age was 27.26 years ( SD = 10.49, Min = 18, Max = 68). Subjects were non-probabilistically selected through convenience and snowball sampling. The online survey was disseminated through emails, social media, university classrooms, etc. The inclusion criteria were: 1) being 18 years or older, 2) being from Honduras, 3) currently living in Honduras, and 4) agreeing to the informed consent.

2.2 Instruments

2.2.1 benevolent childhood experiences.

The Benevolent Childhood Experiences Scale consists of 10 categorical (yes = 1/no = 0) items ( Narayan et al., 2018 ). Each question asks about specific positive occurrences experienced during the first 18 years of life, for example: “Did you have at least one caregiver with whom you felt safe?,” “Did you like school?,” “Did you have a predictable home routine, like regular meals and a regular bedtime?,” among others. Summative BCE scores were calculated; higher totals indicate a higher number of BCEs (Min = 0, Max = 10). The scale has adequate reliability ( ω = 0.70).

2.2.2 Light Triad of personality

The Light Triad Scale (LTS) measures three distinct personality traits through 12 items ( Kaufman et al., 2019 ): 1) Humanism, 2) Faith in Humanity, and 3) Kantianism. Sample items include: “I enjoy listening to people from all walks of life,” “I tend to applaud the successes of other people,” among other affirmations. Each one is rated on a 5-point Likert Scale (1 = totally disagree, 5 = totally agree), with higher summative scores indicating a higher intensity of the trait. Based on the current data, the LTS had a good internal consistency ( ω = 0.85).

2.2.3 Flourishing

The Flourishing Scale (FS) is an 8-item unidimensional questionnaire ( Diener et al., 2010 ). Responses are presented in a 7-point Likert-type questionnaire (1 = totally disagree; 7 = totally agree). Higher summative scores ( Min = 8; Max = 56) indicate a higher self-reported Flourishing. Some items included in the FS are: “I am competent and capable in the activities that are important to me,” “I lead a purposeful and meaningful life,” among others. The FS achieved adequate psychometric properties in the Honduran population, including a high internal consistency (McDonald’s ω = 0.89), test–retest reliability, convergent and divergent validity, as well as an unidimensional factor structure ( Landa-Blanco et al., 2023 ).

2.3 Ethical considerations

This study adhered to ethical guidelines to ensure the protection and welfare of the participants. Before participation, subjects were provided with a clear explanation of the study’s purpose, procedures, risks, benefits, and their right to withdraw during the data collection. They were informed that their participation was voluntary. The questionnaire used in this study was designed to maintain the anonymity of the participants; no personally identifiable information was collected during the data collection process. Results were reported at the group level, and no personally identifiable information was disclosed in any form of dissemination.

At the beginning and end of the survey, participants were presented with a link to the “UNAH Te Escucha,” an online real-time chat platform that provides psychological assistance to the Honduran population. The School of Psychological Sciences of the National Autonomous University of Honduras (UNAH) runs this free-of-charge website. The study was approved by the Research Ethics Committee of the Faculty of Social Sciences of the UNAH (Macrostudy-CEIFCS-P1-2023).

2.4 Data analyses

First, summative scores were calculated for all variables. The internal consistency of each scale was determined using McDonald’s ω . Then, descriptive statistics were used, specifically, Mean ( M ) scores, Standard Deviations ( SD ), absolute and relative frequencies. Two-group comparisons were made using Welch’s t -test and Cohen’s d (as effect size estimate). Categorical associations were determined by a chi-square test ( χ 2 ) and Contingency Coefficients ( CC ). Pearson’s r coefficient was used to assess the bidirectional relationship between variables. A directional assessment was made through mediation analysis, inputting the BCE score as an independent variable. Mediators included Faith in Humanity, Humanism, and Kantianism; Flourishing was set as the model’s final outcome.

To validate the regression models within the mediation framework, Goldfeld-Quandt and Harrison-McCabe tests were employed to assess homoscedasticity. Both tests yielded high p -values (0.891 and 0.893, respectively), indicating no significant evidence of heteroscedasticity or model misspecification. Consequently, the regression model is deemed adequately specified, adhering to constant variance assumptions and appropriate functional form. The Durbin-Watson test statistic ( DW = 2.0739, p = 0.426) revealed no significant evidence of autocorrelation in the residuals; the assumption of independent errors in the regression model is supported. The Variance Inflation Factor ( VIF ) values for the predictors in the mediation model are as follows: BCE = 1.0922, Faith in Humanity = 1.4115, Humanism = 1.6656, and Kantianism = 1.4104. These values indicate low levels of multicollinearity among the predictors, as all VIF values are below the commonly accepted threshold of 10. Therefore, the mediation model is not significantly affected by multicollinearity. All hypotheses were tested at a 95% confidence level using Jamovi ( The Jamovi Project, 2023 ).

On average, respondents reported 7.34 ( SD = 1.76) Benevolent Childhood Experiences. The number of reported BCEs did not vary significantly between men ( M = 7.47; SD = 1.56) and women ( M = 7.26; SD = 1.87); t = 121, p = 0.226, d = −0.12. However, specific BCEs were categorically associated with subjects’ sex. For instance, having at least one teacher who cared about the respondent ( χ 2 = 4.28 , p = 0.039 , CC = 0 .10), having opportunities to have a good time ( χ 2 = 8.84 , p = 0.003 , CC = 0 .15), and liking/feeling comfortable with oneself ( χ 2 = 5.88 , p = 0.015 , CC = 0 .12); with a higher proportion of men reporting the presence of mentioned BCE, see Table 1 .

Table 1 . Prevalence of BCE compared between men and women.

Flourishing was significantly higher for participants who reported the presence of BCEs ( p < 0.05); this holds for all individual BCE items included in the study, see Table 2 . The largest effect size was achieved for the difference in Flourishing scores between those who reported they liked going to school as a child and those who disliked it ( d = −1.17). Medium effect sizes were detected for the following indicators ( d > |0.50|): having at least one good friend ( d = −0.62), good neighbors ( d = −0.63), having comfortable beliefs ( d = −0.75), liking/feeling comfortable with oneself ( d = −0.70), and having a predictable home routine ( d = −0.58).

Table 2 . Comparisons in Flourishing and Light Triad scores based on BCE experiences.

On the other hand, Faith in Humanity scores are higher for those who reported the presence of having at least one good friend ( d = −0.41), comfortable beliefs ( d = −0.37), liking school ( d = −0.91), good neighbors ( d = −0.47), having an adult supporter/adviser ( d = −0.41), opportunities for a good time ( d = −0.41), liking/feeling comfortable with oneself ( d = −0.39), and having a predictable home routine ( d = −0.46). Humanism and Kantianism were higher for those who liked attending school and had an adult advisor/supporter.

The number of reported BCEs correlates positively and significantly with Flourishing ( r = 0.43; p < 0.001) and all the Light Triad traits ( p < 0.01). Flourishing scores also have significant positive associations with the Light Triad ( p < 0.001), see Table 3 .

Table 3 . Bidirectional correlations between variables.

The number of BCE had a significant total ( β = 0.43; p < 0.001) and direct effect ( β = 0.28; p < 0.001) on Flourishing scores. However, significant indirect effects were also identified. Faith in Humanity ( β = 0.09, p < 0.001) and Humanism ( β = 0.06, p < 0.001), not Kantianism ( β = 0.01, p = 0.197) mediated the relationship between BCE and Flourishing. At a component level, BCE score significantly explained all Light Triad traits ( p < 0.01). Faith in Humanity ( β = 0.31, p < 0.001) and Humanism ( β = 0.27, p < 0.001), not Kantianism ( β = 0.06, p = 0.139), directly explained Flourishing scores. Overall, the model accounted for 45% of the variance in Flourishing, see Table 4 .

Table 4 . Mediation analysis of BCE effects on Flourishing.

4 Discussion

The results of this study have several theoretical and practical implications for the field of positive psychology. First, the findings suggest that Benevolent Childhood Experiences are positively associated with Flourishing and the Light Triad traits. The Light Triad traits are considered positive personality characteristics that include Kantianism (respecting the dignity of all individuals), Humanism (valuing the well-being of others), and Faith in Humanity (believing in the inherent goodness of people). The fact that BCEs have a significant positive relationship with these traits underscores the importance of a positive childhood environment in fostering healthy personality traits.

Moreover, the study reveals that BCEs directly affect Flourishing scores. BCEs, such as having good friends or liking school, were associated with higher Flourishing, indicating that a positive childhood environment is important for one’s overall well-being. Furthermore, the indirect effects of BCEs on Flourishing via Faith in Humanity and Humanism suggest that these personality traits may mediate between BCEs and Flourishing.

Individuals reporting BCEs may develop greater Faith in Humanity and hold positive perceptions of others due to several potential underlying mechanisms. First, BCEs involving nurturing relationships and support from caregivers may foster a sense of trust and security, leading individuals to view others as reliable and compassionate. Second, positive childhood experiences can contribute to developing a positive self-concept, allowing individuals to project their positive attributes onto others and perceive them favorably. Third, BCEs may enhance individuals’ social and emotional competence, promoting empathy and understanding toward others, and reinforcing the belief in the inherent goodness and value of people ( AlShawi and Lafta, 2015 ; Lekaviciene and Antiniene, 2016 ; Berduzco-Torres et al., 2020 ; Streit et al., 2020 ). These factors collectively contribute to a more positive worldview.

The examination of Kantianism within the Light Triad framework in this study has provided valuable insights, although conclusive results remain elusive. While a positive correlation between Kantianism and Flourishing implies a possible connection between the concept of inherent human worth and well-being, the lack of a significant direct effect and its comparatively weaker mediating role, in contrast to Faith in Humanity and Humanism, calls for deeper scrutiny. The varying effects of Kantianism, Faith in Humanity, and Humanism on Flourishing are likely influenced by their unique cognitive intricacies and socialization mechanisms. Faith in Humanity and Humanism are likely shaped by positive interpersonal experiences and cultural norms promoting empathy ( Neumann et al., 2020 ; Ramos-Vera et al., 2023 ), while Kantianism requires individuals to engage in complex moral reasoning, which may not directly correlate with self-reported well-being. Furthermore, differences in measurement accuracy could contribute to these observed variations, with Kantianism posing particular challenges for assessment. By gaining a deeper understanding of the mechanisms underlying Kantianism’s influence on Flourishing, we can refine interventions aimed at enhancing well-being.

On the other hand, the fact that specific BCEs were associated with subjects’ sex indicates that there may be sex-based differences in how individuals experience and perceive their childhood environment. For example, a higher proportion of men reported having opportunities to have a good time or liking/feeling comfortable with themselves as BCEs. Additionally, previous studies suggest that Honduran women have experienced more ACEs than men ( Kappel et al., 2021 ). Therefore, public interventions should aim to minimize the difference between boys and girls in terms of BCEs.

The study also has practical implications for clinical psychology and schools. The results suggest that interventions promoting BCEs, such as fostering positive relationships with teachers, creating opportunities for socializing, or promoting a sense of comfort and acceptance, may contribute to positive mental health and well-being outcomes. The findings suggest that schools may play a crucial role in promoting a positive childhood environment by providing opportunities for socialization and positive relationships with teachers.

School support provides a nurturing environment that enhances a child’s emotional intelligence, sense of belonging, competence, and positive relationships, foundational elements for healthy psychological development ( Puertas Molero et al., 2020 ; Gramaxo et al., 2023 ). Liking school reflects positive experiences, engagement, and a sense of purpose, contributing to intrinsic motivation and developing a positive worldview. These positive school experiences can foster social–emotional competencies ( Graham et al., 2022 ), including empathy and prosocial behaviors, which in turn cultivate Faith in Humanity and the perception of people as inherently valuable and deserving of dignity. Such early experiences lay the groundwork for positive adult outcomes, contributing to overall Flourishing and forming positive beliefs about Humanity. This highlights the importance of incorporating socioemotional education within the school setting ( Govorova et al., 2020 ) and universal positive mental health screenings ( Cortés-Ramos and Landa-Blanco, 2021 ).

In this sense, previous research suggests that school connectedness is linked to reducing students’ anxiety and depression symptoms ( Raniti et al., 2022 ). Since teachers play an important role in forming BCEs, public policy should also target teachers’ well-being to promote students’ mental health. In this sense, teacher well-being has been associated with higher student well-being and lower distress scores ( Harding et al., 2019 ).

Several limitations should be considered when interpreting the findings of this study. First, the use of self-reported data is susceptible to recall bias and social desirability bias. Participants may have had difficulty accurately recalling their childhood experiences, potentially leading to inaccuracies in reporting BCEs, Light Triad traits, and Flourishing. Second, the study’s reliance on a non-probabilistic sampling method limits the generalizability of the findings to the broader population of Honduran adults. The sample may not represent the entire population, and the results may not apply to individuals with different socio-demographic characteristics or from other cultural backgrounds. Replication of our findings across diverse populations is crucial for assessing robustness. Third, while we employed a mediation model for analysis, which yielded informative results, its ability to fully capture the complexity of these relationships may be limited. In future research, utilizing Structural Equation Models (SEM) could offer a more comprehensive understanding by better controlling for predictors and mediators. Fourth, although our study identified significant direct and indirect effects of BCEs on Flourishing, the model’s explanatory power was modest. Future research could explore additional factors to elucidate the pathways through which BCEs influence Flourishing. Fifth, our study’s exclusive focus on quantitative data overlooks qualitative perspectives that could provide deeper insights into participants’ experiences. Incorporating qualitative research methods could enrich our understanding of the underlying mechanisms.

In light of these limitations, future research endeavors should leverage more sophisticated statistical techniques, integrate qualitative methodologies, and expand the scope of investigation to deepen our understanding of these complex constructs. Additionally, longitudinal studies are essential for uncovering temporal relationships and comprehending the developmental trajectories of BCEs, Light Triad traits, and Flourishing. Exploring the interaction of Kantianism with other personality traits and cultural contexts could provide valuable insights into promoting positive outcomes in adulthood and enhancing Flourishing.

In conclusion, this study underscores the significance of Benevolent Childhood Experiences in fostering Flourishing and positive personality traits, particularly highlighting the potential of the Light Triad framework in understanding well-being. While the specific role of Kantianism warrants further investigation, Faith in Humanity and Humanism emerge as pivotal pathways for nurturing Flourishing through positive childhood experiences. Moving forward, it is crucial for future research to assess the long-term societal benefits of policies aimed at promoting positive childhood experiences. Initiatives focusing on positive mental health, enhancing educational accessibility, and fostering intergenerational relationships hold promise for cultivating a more conducive childhood environment.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Comité de Ética en Investigación de la Facultad de Ciencias Sociales (UNAH). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

ML-B: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing. TH: Formal analysis, Investigation, Writing – original draft. HE: Formal analysis, Investigation, Writing – original draft. KG: Formal analysis, Investigation, Writing – original draft. SM: Formal analysis, Investigation, Writing – original draft. AC-R: Conceptualization, Methodology, Supervision, Validation, Writing – original draft.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: Benevolent Childhood Experiences, Flourishing, Light Triad, Humanism, Faith in Humanity, Kantianism

Citation: Landa-Blanco M, Herrera T, Espinoza H, Girón K, Moncada S and Cortés-Ramos A (2024) The impact of Benevolent Childhood Experiences on adult Flourishing: the mediating role of Light Triad traits. Front. Psychol . 15:1320169. doi: 10.3389/fpsyg.2024.1320169

Received: 12 October 2023; Accepted: 15 April 2024; Published: 24 April 2024.

Reviewed by:

Copyright © 2024 Landa-Blanco, Herrera, Espinoza, Girón, Moncada and Cortés-Ramos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miguel Landa-Blanco, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

COMMENTS

Research Hypothesis In Psychology: Types, & Examples
The research hypothesis is often called the alternative or experimental hypothesis in experimental research. It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes. ... Social Psychology: ...
Hypothesis: Definition, Examples, and Types
In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior. Remember, a hypothesis does not have to be correct. ... There are two basic types of research methods: descriptive research and experimental research. Descriptive Research Methods .
Developing a Hypothesis
Theories and Hypotheses. Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes ...
2.4 Developing a Hypothesis
A hypothesis, on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. ... Journal of Experimental Social Psychology, 2, 160-168.
How the Experimental Method Works in Psychology
The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis. For example, researchers may want to learn how different visual patterns may impact our perception.
Understanding Experimental Hypotheses in Psychology
An experimental hypothesis in psychology is a statement that proposes a relationship between variables and is essential for guiding empirical research and testing theories. It serves as the foundation of scientific investigations, providing a clear direction for researchers to validate or refute concepts through systematic experimentation. ...
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Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
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2.3 Analyzing Findings
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Experimental psychology
Experimental psychology emerged as a modern academic discipline in the 19th century when Wilhelm Wundt introduced a mathematical and experimental approach to the field. ... Ideally, one hypothesis may be confirmed and all the others rejected. However, the data may also be consistent with several hypotheses, a result that calls for further ...
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Experimental Research
Designing an Experiment. The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this ...
Quantifying Support for the Null Hypothesis in Psychology: An Empirical
results constitute evidence in favor of the null hypothesis. First, we examined all nonsignificant findings mentioned in the abstracts of the 2015 volumes of Psychonomic Bulletin & Review, Journal of Experimental Psychology: General, and Psychological Science (N = 137). In 72% of these cases, nonsignificant results were misinterpreted, in that ...
PDF Statistical Evidence in Experimental Psychology: An Empirical
855 recently published t tests in psychology. Our comparison yields two main results: First, although p values and Bayes factors almost always agree about what hypothesis is better supported by the data, the measures often disagree about the strength of this support; for 70% of the data sets for which the p value falls between 0.01 and
Intro to Methodologies
1. Experimental Methodology: The experimental method involves manipulating one variable (independent variable) to observe the effect it has on another variable (dependent variable), while controlling for extraneous variables. It is used to establish cause-and-effect relationships between variables in controlled laboratory settings.
How Does Experimental Psychology Study Behavior?
The experimental method in psychology helps us learn more about how people think and why they behave the way they do. Experimental psychologists can research a variety of topics using many different experimental methods. Each one contributes to what we know about the mind and human behavior. 4 Sources.
7.2.2 Hypothesis
The Experimental Hypothesis: Directional A directional experimental hypothesis (also known as one-tailed) predicts the direction of the change/difference (it anticipates more specifically what might happen); A directional hypothesis is usually used when there is previous research which support a particular theory or outcome i.e. what a researcher might expect to happen
Hypotheses AO1 AO2
EXEMPLAR ESSAYHow to write a 8-mark answer. Assess how hypotheses are used in the Cognitive Approach. (8 marks) A 8-mark "apply" question awards 4 marks for describing the use of hypotheses (AO1) and 4 marks for applying the Cognitive Approach to this (AO2). You need a conclusion to get a mark in the top band (7-8 marks).
People are surprisingly hesitant to reach out to old friends
To test this hypothesis, ... The nature and function of self-esteem: sociometer theory. in Advances in Experimental Social Psychology, Vol. 32, 1-62 (Academic Press, 2000).
Frontiers
1 School of Psychological Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras; 2 Faculty of Education and Sport Sciences, University of Granada, Melilla, Spain; The literature has well documented the relationship between Adverse Childhood Experiences, personality traits, and well-being. However, less is known about how Benevolent Childhood Experiences (BCEs) relate to ...

Developing a Hypothesis

Learning Objectives

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Share This Book

2.4 Developing a Hypothesis

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Key Takeaways

Share This Book

Understanding Experimental Hypotheses in Psychology

What Is an Experimental Hypothesis?

Why Is an Experimental Hypothesis Important in Psychology?

What Are the Components of an Experimental Hypothesis?

Independent Variable

Dependent Variable

Control Variables

What Are the Types of Experimental Hypotheses?

Directional Hypothesis

Non-directional Hypothesis

Null Hypothesis

How Is an Experimental Hypothesis Formulated?

Review of Literature

Identifying Variables

Making Predictions

What Is the Role of an Experimental Hypothesis in Research Design?

How Is an Experimental Hypothesis Tested?

Choosing a Research Design

Collecting Data

Analyzing Data

What Are the Possible Outcomes of an Experimental Hypothesis?

Frequently Asked Questions

How is an experimental hypothesis different from a research question?

What are the characteristics of a good experimental hypothesis?

Can an experimental hypothesis be proven to be true?

Why is it important to have a well-formed experimental hypothesis?

Can an experimental hypothesis change during the course of a study?

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