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Aims and Hypotheses

Last updated 22 Mar 2021

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Observations of events or behaviour in our surroundings provoke questions as to why they occur. In turn, one or multiple theories might attempt to explain a phenomenon, and investigations are consequently conducted to test them. One observation could be that athletes tend to perform better when they have a training partner, and a theory might propose that this is because athletes are more motivated with peers around them.

The aim of an investigation, driven by a theory to explain a given observation, states the intent of the study in general terms. Continuing the above example, the consequent aim might be “to investigate the effect of having a training partner on athletes’ motivation levels”.

The theory attempting to explain an observation will help to inform hypotheses - predictions of an investigation’s outcome that make specific reference to the independent variables (IVs) manipulated and dependent variables (DVs) measured by the researchers.

There are two types of hypothesis:

- H 1 – Research hypothesis
- H 0 – Null hypothesis

H 1 – The Research Hypothesis

This predicts a statistically significant effect of an IV on a DV (i.e. an experiment), or a significant relationship between variables (i.e. a correlation study), e.g.

In an experiment: “Athletes who have a training partner are likely to score higher on a questionnaire measuring motivation levels than athletes who train alone.”
In a correlation study: ‘There will be a significant positive correlation between athletes’ motivation questionnaire scores and the number of partners athletes train with.”

The research hypothesis will be directional (one-tailed) if theory or existing evidence argues a particular ‘direction’ of the predicted results, as demonstrated in the two hypothesis examples above.

Non-directional (two-tailed) research hypotheses do not predict a direction, so here would simply predict “a significant difference” between questionnaire scores in athletes who train alone and with a training partner (in an experiment), or “a significant relationship” between questionnaire scores and number of training partners (in a correlation study).

H 0 – The Null Hypothesis

This predicts that a statistically significant effect or relationship will not be found, e.g.

In an experiment: “There will be no significant difference in motivation questionnaire scores between athletes who train with and without a training partner.”
In a correlation study: “There will be no significant relationship between motivation questionnaire scores and the number of partners athletes train with.”

When the investigation concludes, analysis of results will suggest that either the research hypothesis or null hypothesis can be retained, with the other rejected. Ultimately this will either provide evidence to support of refute the theory driving a hypothesis, and may lead to further research in the field.

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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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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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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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9 Chapter 9 Hypothesis testing

The first unit was designed to prepare you for hypothesis testing. In the first chapter we discussed the three major goals of statistics:

Describe: connects to unit 1 with descriptive statistics and graphing
Decide: connects to unit 1 knowing your data and hypothesis testing
Predict: connects to hypothesis testing and unit 3

The remaining chapters will cover many different kinds of hypothesis tests connected to different inferential statistics. Needless to say, hypothesis testing is the central topic of this course. This lesson is important but that does not mean the same thing as difficult. There is a lot of new language we will learn about when conducting a hypothesis test. Some of the components of a hypothesis test are the topics we are already familiar with:

Test statistics
Probability
Distribution of sample means

Hypothesis testing is an inferential procedure that uses data from a sample to draw a general conclusion about a population. It is a formal approach and a statistical method that uses sample data to evaluate hypotheses about a population. When interpreting a research question and statistical results, a natural question arises as to whether the finding could have occurred by chance. Hypothesis testing is a statistical procedure for testing whether chance (random events) is a reasonable explanation of an experimental finding. Once you have mastered the material in this lesson you will be used to solving hypothesis testing problems and the rest of the course will seem much easier. In this chapter, we will introduce the ideas behind the use of statistics to make decisions – in particular, decisions about whether a particular hypothesis is supported by the data.

Logic and Purpose of Hypothesis Testing

The statistician Ronald Fisher explained the concept of hypothesis testing with a story of a lady tasting tea. Fisher was a statistician from London and is noted as the first person to formalize the process of hypothesis testing. His elegantly simple “Lady Tasting Tea” experiment demonstrated the logic of the hypothesis test.

Figure 1. A depiction of the lady tasting tea Photo Credit

Fisher would often have afternoon tea during his studies. He usually took tea with a woman who claimed to be a tea expert. In particular, she told Fisher that she could tell which was poured first in the teacup, the milk or the tea, simply by tasting the cup. Fisher, being a scientist, decided to put this rather bizarre claim to the test. The lady accepted his challenge. Fisher brought her 8 cups of tea in succession; 4 cups would be prepared with the milk added first, and 4 with the tea added first. The cups would be presented in a random order unknown to the lady.

The lady would take a sip of each cup as it was presented and report which ingredient she believed was poured first. Using the laws of probability, Fisher determined the chances of her guessing all 8 cups correctly was 1/70, or about 1.4%. In other words, if the lady was indeed guessing there was a 1.4% chance of her getting all 8 cups correct. On the day of the experiment, Fisher had 8 cups prepared just as he had requested. The lady drank each cup and made her decisions for each one.

After the experiment, it was revealed that the lady got all 8 cups correct! Remember, had she been truly guessing, the chance of getting this result was 1.4%. Since this probability was so low , Fisher instead concluded that the lady could indeed differentiate between the milk or the tea being poured first. Fisher’s original hypothesis that she was just guessing was demonstrated to be false and was therefore rejected. The alternative hypothesis, that the lady could truly tell the cups apart, was then accepted as true.

This story demonstrates many components of hypothesis testing in a very simple way. For example, Fisher started with a hypothesis that the lady was guessing. He then determined that if she was indeed guessing, the probability of guessing all 8 right was very small, just 1.4%. Since that probability was so tiny, when she did get all 8 cups right, Fisher determined it was extremely unlikely she was guessing. A more reasonable conclusion was that the lady had the skill to tell the cups apart.

In hypothesis testing, we will always set up a particular hypothesis that we want to demonstrate to be true. We then use probability to determine the likelihood of our hypothesis is correct. If it appears our original hypothesis was wrong, we reject it and accept the alternative hypothesis. The alternative hypothesis is usually the opposite of our original hypothesis. In Fisher’s case, his original hypothesis was that the lady was guessing. His alternative hypothesis was the lady was not guessing.

This result does not prove that he does; it could be he was just lucky and guessed right 13 out of 16 times. But how plausible is the explanation that he was just lucky? To assess its plausibility, we determine the probability that someone who was just guessing would be correct 13/16 times or more. This probability can be computed to be 0.0106. This is a pretty low probability, and therefore someone would have to be very lucky to be correct 13 or more times out of 16 if they were just guessing. A low probability gives us more confidence there is evidence Bond can tell whether the drink was shaken or stirred. There is also still a chance that Mr. Bond was very lucky (more on this later!). The hypothesis that he was guessing is not proven false, but considerable doubt is cast on it. Therefore, there is strong evidence that Mr. Bond can tell whether a drink was shaken or stirred.

You may notice some patterns here:

We have 2 hypotheses: the original (researcher prediction) and the alternative
We collect data
We determine how likley or unlikely the original hypothesis is to occur based on probability.
We determine if we have enough evidence to support the original hypothesis and draw conclusions.

Now let’s being in some specific terminology:

Null hypothesis : In general, the null hypothesis, written H 0 (“H-naught”), is the idea that nothing is going on: there is no effect of our treatment, no relation between our variables, and no difference in our sample mean from what we expected about the population mean. The null hypothesis indicates that an apparent effect is due to chance. This is always our baseline starting assumption, and it is what we (typically) seek to reject . For mathematical notation, one uses =).

Alternative hypothesis : If the null hypothesis is rejected, then we will need some other explanation, which we call the alternative hypothesis, H A or H 1 . The alternative hypothesis is simply the reverse of the null hypothesis. Thus, our alternative hypothesis is the mathematical way of stating our research question. In general, the alternative hypothesis (also called the research hypothesis)is there is an effect of treatment, the relation between variables, or differences in a sample mean compared to a population mean. The alternative hypothesis essentially shows evidence the findings are not due to chance. It is also called the research hypothesis as this is the most common outcome a researcher is looking for: evidence of change, differences, or relationships. There are three options for setting up the alternative hypothesis, depending on where we expect the difference to lie. The alternative hypothesis always involves some kind of inequality (≠not equal, >, or <).

If we expect a specific direction of change/differences/relationships, which we call a directional hypothesis , then our alternative hypothesis takes the form based on the research question itself. One would expect a decrease in depression from taking an anti-depressant as a specific directional hypothesis. Or the direction could be larger, where for example, one might expect an increase in exam scores after completing a student success exam preparation module. The directional hypothesis (2 directions) makes up 2 of the 3 alternative hypothesis options. The other alternative is to state there are differences/changes, or a relationship but not predict the direction. We use a non-directional alternative hypothesis (typically see ≠ for mathematical notation).

Probability value (p-value) : the probability of a certain outcome assuming a certain state of the world. In statistics, it is conventional to refer to possible states of the world as hypotheses since they are hypothesized states of the world. Using this terminology, the probability value is the probability of an outcome given the hypothesis. It is not the probability of the hypothesis given the outcome. It is very important to understand precisely what the probability values mean. In the James Bond example, the computed probability of 0.0106 is the probability he would be correct on 13 or more taste tests (out of 16) if he were just guessing. It is easy to mistake this probability of 0.0106 as the probability he cannot tell the difference. This is not at all what it means. The probability of 0.0106 is the probability of a certain outcome (13 or more out of 16) assuming a certain state of the world (James Bond was only guessing).

A low probability value casts doubt on the null hypothesis. How low must the probability value be in order to conclude that the null hypothesis is false? Although there is clearly no right or wrong answer to this question, it is conventional to conclude the null hypothesis is false if the probability value is less than 0.05 (p < .05). More conservative researchers conclude the null hypothesis is false only if the probability value is less than 0.01 (p<.01). When a researcher concludes that the null hypothesis is false, the researcher is said to have rejected the null hypothesis. The probability value below which the null hypothesis is rejected is called the α level or simply α (“alpha”). It is also called the significance level . If α is not explicitly specified, assume that α = 0.05.

Decision-making is part of the process and we have some language that goes along with that. Importantly, null hypothesis testing operates under the assumption that the null hypothesis is true unless the evidence shows otherwise. We (typically) seek to reject the null hypothesis, giving us evidence to support the alternative hypothesis . If the probability of the outcome given the hypothesis is sufficiently low, we have evidence that the null hypothesis is false. Note that all probability calculations for all hypothesis tests center on the null hypothesis. In the James Bond example, the null hypothesis is that he cannot tell the difference between shaken and stirred martinis. The probability value is low that one is able to identify 13 of 16 martinis as shaken or stirred (0.0106), thus providing evidence that he can tell the difference. Note that we have not computed the probability that he can tell the difference.

The specific type of hypothesis testing reviewed is specifically known as null hypothesis statistical testing (NHST). We can break the process of null hypothesis testing down into a number of steps a researcher would use.

Formulate a hypothesis that embodies our prediction ( before seeing the data )
Specify null and alternative hypotheses
Collect some data relevant to the hypothesis
Compute a test statistic
Identify the criteria probability (or compute the probability of the observed value of that statistic) assuming that the null hypothesis is true
Drawing conclusions. Assess the “statistical significance” of the result

Steps in hypothesis testing

Step 1: formulate a hypothesis of interest.

The researchers hypothesized that physicians spend less time with obese patients. The researchers hypothesis derived from an identified population. In creating a research hypothesis, we also have to decide whether we want to test a directional or non-directional hypotheses. Researchers typically will select a non-directional hypothesis for a more conservative approach, particularly when the outcome is unknown (more about why this is later).

Step 2: Specify the null and alternative hypotheses

Can you set up the null and alternative hypotheses for the Physician’s Reaction Experiment?

Step 3: Determine the alpha level.

For this course, alpha will be given to you as .05 or .01. Researchers will decide on alpha and then determine the associated test statistic based from the sample. Researchers in the Physician Reaction study might set the alpha at .05 and identify the test statistics associated with the .05 for the sample size. Researchers might take extra precautions to be more confident in their findings (more on this later).

Step 4: Collect some data

For this course, the data will be given to you. Researchers collect the data and then start to summarize it using descriptive statistics. The mean time physicians reported that they would spend with obese patients was 24.7 minutes as compared to a mean of 31.4 minutes for normal-weight patients.

Step 5: Compute a test statistic

We next want to use the data to compute a statistic that will ultimately let us decide whether the null hypothesis is rejected or not. We can think of the test statistic as providing a measure of the size of the effect compared to the variability in the data. In general, this test statistic will have a probability distribution associated with it, because that allows us to determine how likely our observed value of the statistic is under the null hypothesis.

To assess the plausibility of the hypothesis that the difference in mean times is due to chance, we compute the probability of getting a difference as large or larger than the observed difference (31.4 – 24.7 = 6.7 minutes) if the difference were, in fact, due solely to chance.

Step 6: Determine the probability of the observed result under the null hypothesis

Using methods presented in later chapters, this probability associated with the observed differences between the two groups for the Physician’s Reaction was computed to be 0.0057. Since this is such a low probability, we have confidence that the difference in times is due to the patient’s weight (obese or not) (and is not due to chance). We can then reject the null hypothesis (there are no differences or differences seen are due to chance).

Keep in mind that the null hypothesis is typically the opposite of the researcher’s hypothesis. In the Physicians’ Reactions study, the researchers hypothesized that physicians would expect to spend less time with obese patients. The null hypothesis that the two types of patients are treated identically as part of the researcher’s control of other variables. If the null hypothesis were true, a difference as large or larger than the sample difference of 6.7 minutes would be very unlikely to occur. Therefore, the researchers rejected the null hypothesis of no difference and concluded that in the population, physicians intend to spend less time with obese patients.

This is the step where NHST starts to violate our intuition. Rather than determining the likelihood that the null hypothesis is true given the data, we instead determine the likelihood under the null hypothesis of observing a statistic at least as extreme as one that we have observed — because we started out by assuming that the null hypothesis is true! To do this, we need to know the expected probability distribution for the statistic under the null hypothesis, so that we can ask how likely the result would be under that distribution. This will be determined from a table we use for reference or calculated in a statistical analysis program. Note that when I say “how likely the result would be”, what I really mean is “how likely the observed result or one more extreme would be”. We need to add this caveat as we are trying to determine how weird our result would be if the null hypothesis were true, and any result that is more extreme will be even more weird, so we want to count all of those weirder possibilities when we compute the probability of our result under the null hypothesis.

Let’s review some considerations for Null hypothesis statistical testing (NHST)!

Null hypothesis statistical testing (NHST) is commonly used in many fields. If you pick up almost any scientific or biomedical research publication, you will see NHST being used to test hypotheses, and in their introductory psychology textbook, Gerrig & Zimbardo (2002) referred to NHST as the “backbone of psychological research”. Thus, learning how to use and interpret the results from hypothesis testing is essential to understand the results from many fields of research.

It is also important for you to know, however, that NHST is flawed, and that many statisticians and researchers think that it has been the cause of serious problems in science, which we will discuss in further in this unit. NHST is also widely misunderstood, largely because it violates our intuitions about how statistical hypothesis testing should work. Let’s look at an example to see this.

There is great interest in the use of body-worn cameras by police officers, which are thought to reduce the use of force and improve officer behavior. However, in order to establish this we need experimental evidence, and it has become increasingly common for governments to use randomized controlled trials to test such ideas. A randomized controlled trial of the effectiveness of body-worn cameras was performed by the Washington, DC government and DC Metropolitan Police Department in 2015-2016. Officers were randomly assigned to wear a body-worn camera or not, and their behavior was then tracked over time to determine whether the cameras resulted in less use of force and fewer civilian complaints about officer behavior.

Before we get to the results, let’s ask how you would think the statistical analysis might work. Let’s say we want to specifically test the hypothesis of whether the use of force is decreased by the wearing of cameras. The randomized controlled trial provides us with the data to test the hypothesis – namely, the rates of use of force by officers assigned to either the camera or control groups. The next obvious step is to look at the data and determine whether they provide convincing evidence for or against this hypothesis. That is: What is the likelihood that body-worn cameras reduce the use of force, given the data and everything else we know?

It turns out that this is not how null hypothesis testing works. Instead, we first take our hypothesis of interest (i.e. that body-worn cameras reduce use of force), and flip it on its head, creating a null hypothesis – in this case, the null hypothesis would be that cameras do not reduce use of force. Importantly, we then assume that the null hypothesis is true. We then look at the data, and determine how likely the data would be if the null hypothesis were true. If the data are sufficiently unlikely under the null hypothesis that we can reject the null in favor of the alternative hypothesis which is our hypothesis of interest. If there is not sufficient evidence to reject the null, then we say that we retain (or “fail to reject”) the null, sticking with our initial assumption that the null is true.

Understanding some of the concepts of NHST, particularly the notorious “p-value”, is invariably challenging the first time one encounters them, because they are so counter-intuitive. As we will see later, there are other approaches that provide a much more intuitive way to address hypothesis testing (but have their own complexities).

Step 7: Assess the “statistical significance” of the result. Draw conclusions.

The next step is to determine whether the p-value that results from the previous step is small enough that we are willing to reject the null hypothesis and conclude instead that the alternative is true. In the Physicians Reactions study, the probability value is 0.0057. Therefore, the effect of obesity is statistically significant and the null hypothesis that obesity makes no difference is rejected. It is very important to keep in mind that statistical significance means only that the null hypothesis of exactly no effect is rejected; it does not mean that the effect is important, which is what “significant” usually means. When an effect is significant, you can have confidence the effect is not exactly zero. Finding that an effect is significant does not tell you about how large or important the effect is.

How much evidence do we require and what considerations are needed to better understand the significance of the findings? This is one of the most controversial questions in statistics, in part because it requires a subjective judgment – there is no “correct” answer.

What does a statistically significant result mean?

There is a great deal of confusion about what p-values actually mean (Gigerenzer, 2004). Let’s say that we do an experiment comparing the means between conditions, and we find a difference with a p-value of .01. There are a number of possible interpretations that one might entertain.

Does it mean that the probability of the null hypothesis being true is .01? No. Remember that in null hypothesis testing, the p-value is the probability of the data given the null hypothesis. It does not warrant conclusions about the probability of the null hypothesis given the data.

Does it mean that the probability that you are making the wrong decision is .01? No. Remember as above that p-values are probabilities of data under the null, not probabilities of hypotheses.

Does it mean that if you ran the study again, you would obtain the same result 99% of the time? No. The p-value is a statement about the likelihood of a particular dataset under the null; it does not allow us to make inferences about the likelihood of future events such as replication.

Does it mean that you have found a practially important effect? No. There is an essential distinction between statistical significance and practical significance . As an example, let’s say that we performed a randomized controlled trial to examine the effect of a particular diet on body weight, and we find a statistically significant effect at p<.05. What this doesn’t tell us is how much weight was actually lost, which we refer to as the effect size (to be discussed in more detail). If we think about a study of weight loss, then we probably don’t think that the loss of one ounce (i.e. the weight of a few potato chips) is practically significant. Let’s look at our ability to detect a significant difference of 1 ounce as the sample size increases.

A statistically significant result is not necessarily a strong one. Even a very weak result can be statistically significant if it is based on a large enough sample. This is why it is important to distinguish between the statistical significance of a result and the practical significance of that result. Practical significance refers to the importance or usefulness of the result in some real-world context and is often referred to as the effect size .

Many differences are statistically significant—and may even be interesting for purely scientific reasons—but they are not practically significant. In clinical practice, this same concept is often referred to as “clinical significance.” For example, a study on a new treatment for social phobia might show that it produces a statistically significant positive effect. Yet this effect still might not be strong enough to justify the time, effort, and other costs of putting it into practice—especially if easier and cheaper treatments that work almost as well already exist. Although statistically significant, this result would be said to lack practical or clinical significance.

Be aware that the term effect size can be misleading because it suggests a causal relationship—that the difference between the two means is an “effect” of being in one group or condition as opposed to another. In other words, simply calling the difference an “effect size” does not make the relationship a causal one.

Figure 1 shows how the proportion of significant results increases as the sample size increases, such that with a very large sample size (about 262,000 total subjects), we will find a significant result in more than 90% of studies when there is a 1 ounce difference in weight loss between the diets. While these are statistically significant, most physicians would not consider a weight loss of one ounce to be practically or clinically significant. We will explore this relationship in more detail when we return to the concept of statistical power in Chapter X, but it should already be clear from this example that statistical significance is not necessarily indicative of practical significance.

The proportion of signifcant results for a very small change (1 ounce, which is about .001 standard deviations) as a function of sample size.

Figure 1: The proportion of significant results for a very small change (1 ounce, which is about .001 standard deviations) as a function of sample size.

Challenges with using p-values

Historically, the most common answer to this question has been that we should reject the null hypothesis if the p-value is less than 0.05. This comes from the writings of Ronald Fisher, who has been referred to as “the single most important figure in 20th century statistics” (Efron, 1998 ) :

“If P is between .1 and .9 there is certainly no reason to suspect the hypothesis tested. If it is below .02 it is strongly indicated that the hypothesis fails to account for the whole of the facts. We shall not often be astray if we draw a conventional line at .05 … it is convenient to draw the line at about the level at which we can say: Either there is something in the treatment, or a coincidence has occurred such as does not occur more than once in twenty trials” (Fisher, 1925 )

Fisher never intended p<0.05p < 0.05 to be a fixed rule:

“no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas” (Fisher, 1956 )

Instead, it is likely that p < .05 became a ritual due to the reliance upon tables of p-values that were used before computing made it easy to compute p values for arbitrary values of a statistic. All of the tables had an entry for 0.05, making it easy to determine whether one’s statistic exceeded the value needed to reach that level of significance. Although we use tables in this class, statistical software examines the specific probability value for the calculated statistic.

Assessing Error Rate: Type I and Type II Error

Although there are challenges with p-values for decision making, we will examine a way we can think about hypothesis testing in terms of its error rate. This was proposed by Jerzy Neyman and Egon Pearson:

“no test based upon a theory of probability can by itself provide any valuable evidence of the truth or falsehood of a hypothesis. But we may look at the purpose of tests from another viewpoint. Without hoping to know whether each separate hypothesis is true or false, we may search for rules to govern our behaviour with regard to them, in following which we insure that, in the long run of experience, we shall not often be wrong” (Neyman & Pearson, 1933 )

That is: We can’t know which specific decisions are right or wrong, but if we follow the rules, we can at least know how often our decisions will be wrong in the long run.

To understand the decision-making framework that Neyman and Pearson developed, we first need to discuss statistical decision-making in terms of the kinds of outcomes that can occur. There are two possible states of reality (H0 is true, or H0 is false), and two possible decisions (reject H0, or retain H0). There are two ways in which we can make a correct decision:

We can reject H0 when it is false (in the language of signal detection theory, we call this a hit )
We can retain H0 when it is true (somewhat confusingly in this context, this is called a correct rejection )

There are also two kinds of errors we can make:

We can reject H0 when it is actually true (we call this a false alarm , or Type I error ), Type I error means that we have concluded that there is a relationship in the population when in fact there is not. Type I errors occur because even when there is no relationship in the population, sampling error alone will occasionally produce an extreme result.
We can retain H0 when it is actually false (we call this a miss , or Type II error ). Type II error means that we have concluded that there is no relationship in the population when in fact there is.

Summing up, when you perform a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis H0 and the decision to reject or not. The outcomes are summarized in the following table:

Table 1. The four possible outcomes in hypothesis testing.

The decision is not to reject H0 when H0 is true (correct decision).
The decision is to reject H0 when H0 is true (incorrect decision known as a Type I error ).
The decision is not to reject H0 when, in fact, H0 is false (incorrect decision known as a Type II error ).
The decision is to reject H0 when H0 is false ( correct decision ).

Neyman and Pearson coined two terms to describe the probability of these two types of errors in the long run:

P(Type I error) = αalpha
P(Type II error) = βbeta

That is, if we set αalpha to .05, then in the long run we should make a Type I error 5% of the time. The 𝞪 (alpha) , is associated with the p-value for the level of significance. Again it’s common to set αalpha as .05. In fact, when the null hypothesis is true and α is .05, we will mistakenly reject the null hypothesis 5% of the time. (This is why α is sometimes referred to as the “Type I error rate.”) In principle, it is possible to reduce the chance of a Type I error by setting α to something less than .05. Setting it to .01, for example, would mean that if the null hypothesis is true, then there is only a 1% chance of mistakenly rejecting it. But making it harder to reject true null hypotheses also makes it harder to reject false ones and therefore increases the chance of a Type II error.

In practice, Type II errors occur primarily because the research design lacks adequate statistical power to detect the relationship (e.g., the sample is too small). Statistical power is the complement of Type II error. We will have more to say about statistical power shortly. The standard value for an acceptable level of β (beta) is .2 – that is, we are willing to accept that 20% of the time we will fail to detect a true effect when it truly exists. It is possible to reduce the chance of a Type II error by setting α to something greater than .05 (e.g., .10). But making it easier to reject false null hypotheses also makes it easier to reject true ones and therefore increases the chance of a Type I error. This provides some insight into why the convention is to set α to .05. There is some agreement among researchers that level of α keeps the rates of both Type I and Type II errors at acceptable levels.

The possibility of committing Type I and Type II errors has several important implications for interpreting the results of our own and others’ research. One is that we should be cautious about interpreting the results of any individual study because there is a chance that it reflects a Type I or Type II error. This is why researchers consider it important to replicate their studies. Each time researchers replicate a study and find a similar result, they rightly become more confident that the result represents a real phenomenon and not just a Type I or Type II error.

Test Statistic Assumptions

Last consideration we will revisit with each test statistic (e.g., t-test, z-test and ANOVA) in the coming chapters. There are four main assumptions. These assumptions are often taken for granted in using prescribed data for the course. In the real world, these assumptions would need to be examined, often tested using statistical software.

Assumption of random sampling. A sample is random when each person (or animal) point in your population has an equal chance of being included in the sample; therefore selection of any individual happens by chance, rather than by choice. This reduces the chance that differences in materials, characteristics or conditions may bias results. Remember that random samples are more likely to be representative of the population so researchers can be more confident interpreting the results. Note: there is no test that statistical software can perform which assures random sampling has occurred but following good sampling techniques helps to ensure your samples are random.
Assumption of Independence. Statistical independence is a critical assumption for many statistical tests including the 2-sample t-test and ANOVA. It is assumed that observations are independent of each other often but often this assumption. Is not met. Independence means the value of one observation does not influence or affect the value of other observations. Independent data items are not connected with one another in any way (unless you account for it in your study). Even the smallest dependence in your data can turn into heavily biased results (which may be undetectable) if you violate this assumption. Note: there is no test statistical software can perform that assures independence of the data because this should be addressed during the research planning phase. Using a non-parametric test is often recommended if a researcher is concerned this assumption has been violated.
Assumption of Normality. Normality assumes that the continuous variables (dependent variable) used in the analysis are normally distributed. Normal distributions are symmetric around the center (the mean) and form a bell-shaped distribution. Normality is violated when sample data are skewed. With large enough sample sizes (n > 30) the violation of the normality assumption should not cause major problems (remember the central limit theorem) but there is a feature in most statistical software that can alert researchers to an assumption violation.
Assumption of Equal Variance. Variance refers to the spread or of scores from the mean. Many statistical tests assume that although different samples can come from populations with different means, they have the same variance. Equality of variance (i.e., homogeneity of variance) is violated when variances across different groups or samples are significantly different. Note: there is a feature in most statistical software to test for this.

We will use 4 main steps for hypothesis testing:

Usually the hypotheses concern population parameters and predict the characteristics that a sample should have
Null: Null hypothesis (H0) states that there is no difference, no effect or no change between population means and sample means. There is no difference.
Alternative: Alternative hypothesis (H1 or HA) states that there is a difference or a change between the population and sample. It is the opposite of the null hypothesis.
Set criteria for a decision. In this step we must determine the boundary of our distribution at which the null hypothesis will be rejected. Researchers usually use either a 5% (.05) cutoff or 1% (.01) critical boundary. Recall from our earlier story about Ronald Fisher that the lower the probability the more confident the was that the Tea Lady was not guessing. We will apply this to z in the next chapter.
Compare sample and population to decide if the hypothesis has support
When a researcher uses hypothesis testing, the individual is making a decision about whether the data collected is sufficient to state that the population parameters are significantly different.

Further considerations

The probability value is the probability of a result as extreme or more extreme given that the null hypothesis is true. It is the probability of the data given the null hypothesis. It is not the probability that the null hypothesis is false.

A low probability value indicates that the sample outcome (or one more extreme) would be very unlikely if the null hypothesis were true. We will learn more about assessing effect size later in this unit.

3. A non-significant outcome means that the data do not conclusively demonstrate that the null hypothesis is false. There is always a chance of error and 4 outcomes associated with hypothesis testing.

It is important to take into account the assumptions for each test statistic.

Learning objectives

Having read the chapter, you should be able to:

Identify the components of a hypothesis test, including the parameter of interest, the null and alternative hypotheses, and the test statistic.
State the hypotheses and identify appropriate critical areas depending on how hypotheses are set up.
Describe the proper interpretations of a p-value as well as common misinterpretations.
Distinguish between the two types of error in hypothesis testing, and the factors that determine them.
Describe the main criticisms of null hypothesis statistical testing
Identify the purpose of effect size and power.

Exercises – Ch. 9

In your own words, explain what the null hypothesis is.
What are Type I and Type II Errors?
Why do we phrase null and alternative hypotheses with population parameters and not sample means?
If our null hypothesis is “H0: μ = 40”, what are the three possible alternative hypotheses?
Why do we state our hypotheses and decision criteria before we collect our data?
When and why do you calculate an effect size?

Answers to Odd- Numbered Exercises – Ch. 9

1. Your answer should include mention of the baseline assumption of no difference between the sample and the population.

3. Alpha is the significance level. It is the criteria we use when decided to reject or fail to reject the null hypothesis, corresponding to a given proportion of the area under the normal distribution and a probability of finding extreme scores assuming the null hypothesis is true.

5. μ > 40; μ < 40; μ ≠ 40

7. We calculate effect size to determine the strength of the finding. Effect size should always be calculated when the we have rejected the null hypothesis. Effect size can be calculated for non-significant findings as a possible indicator of Type II error.

Introduction to Statistics for Psychology Copyright © 2021 by Alisa Beyer is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

What Is the Mere Exposure Effect in Psychology?

Why We Like Things We’ve Seen Before

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Archaeology
Ph.D., Psychology, University of California - Santa Barbara
B.A., Psychology and Peace & Conflict Studies, University of California - Berkeley

Would you rather watch a new movie, or an old favorite? Would you rather try a dish you’ve never had at a restaurant, or stick with something you know you’ll like? According to psychologists, there’s a reason why we may prefer the familiar over the novel. Researchers studying the "mere exposure effect" have found that we often prefer things that we’ve seen before over things that are new.

Key Takeaways: Mere Exposure Effect

The mere exposure effect refers to the finding that, the more often people have previously been exposed to something, the more they like it.
Researchers have found that the mere exposure effect occurs even if people do not consciously remember that they have seen the object before.
Although researchers aren’t in agreement about why the mere exposure effect happens, two theories are that having seen something before makes us feel less uncertain, and that things we’ve seen before are easier to interpret.

Key Research

In 1968, social psychologist Robert Zajonc published a landmark paper on the mere exposure effect. Zajonc’s hypothesis was that simply being exposed to something on a repeated basis was enough to make people like that thing. According to Zajonc, people didn’t need to experience a reward or positive outcome while around the object—simply being exposed to the object would be enough to make people like it.

To test this, Zajonc had participants read words in a foreign language out loud. Zajonc varied how often participants read each word (up to 25 repetitions). Next, after reading the words, participants were asked to guess at the meaning of each word by filling out a rating scale (indicating how positive or negative they thought the meaning of the word was). He found that participants liked words that they had said more often, while words that participants hadn’t read at all were rated more negatively, and words that had been read 25 times were rated highest. Just the mere exposure to the word was enough to make participants like it more.

Example of the Mere Exposure Effect

One place where the mere exposure effect occurs is in advertising—in fact, in his original paper, Zajonc mentioned the importance of mere exposure to advertisers. The mere exposure effect explains why seeing the same advertisement multiple times could be more convincing than just seeing it once: that “as seen on TV” product may seem silly the first time you hear about it, but after seeing the ad a few more times, you start to think about buying the product yourself.

Of course, there’s a caveat here: the mere exposure effect doesn’t happen for things we initially dislike—so if you really hate that advertising jingle you just heard, hearing it more won’t cause you to feel inexplicably drawn to the product advertised.

When Does the Mere Exposure Effect Happen?

Since Zajonc’s initial study, numerous researchers have investigated the mere exposure effect. Researchers have found that our liking for a variety of things (including pictures, sounds, foods, and smells) can be increased with repeated exposure, suggesting that the mere exposure effect isn’t limited to just one of our senses. Additionally, researchers have found that the mere exposure effect occurs in studies with human research participants as well as in studies with non-human animals.

One of the most striking findings from this research is that people don’t even have to consciously notice the object in order for the mere exposure effect to occur. In one line of research , Zajonc and his colleagues tested what happened when participants were shown images subliminally. Images were flashed in front of participants for less than one second—quickly enough that the participants were unable to recognize which image they had been shown. The researchers found that participants liked the images better when they had previously seen them (compared to new images). Moreover, participants who were repeatedly shown the same set of images reported being in a more positive mood (compared to participants who only saw each image once). In other words, being subliminally shown a set of images was able to affect participants’ preferences and moods.

In a 2017 study, psychologist R. Matthew Montoya and colleagues conducted a meta-analysis on the mere exposure effect, an analysis combining the results of previous research studies—with a total of over 8,000 research participants. The researchers found that the mere exposure effect did indeed occur when participants were repeatedly exposed to images, but not when participants were repeatedly exposed to sounds (although the researchers point out that this may have had to do with the particular details of these studies, such as the types of sounds researchers used, and that some individual studies did find that the mere exposure effect occurs for sounds). Another key finding from this meta-analysis was that participants eventually started to like objects less after many repeated exposures. In other words, a smaller number of repeated exposures will make you like something more—but, if the repeated exposures continue, you could eventually get tired of it.

Explanations for the Mere Exposure Effect

In the decades since Zajonc published his paper on the mere exposure effect, researchers have suggested several theories to explain why the effect happens. Two of the leading theories are that mere exposure makes us feel less uncertain, and that it increases what psychologists call perceptual fluency .

Uncertainty Reduction

According to Zajonc and his colleagues, the mere exposure effect occurs because being repeatedly exposed to the same person, image, or object reduces the uncertainty we feel. According to this idea (based in evolutionary psychology ), we’re primed to be cautious around new things, since they could be dangerous to us. However, when we see the same thing over and over and nothing bad happens, we start to realize that there’s nothing to be afraid of. In other words, the mere exposure effect occurs because we feel more positively about something familiar compared to something that is new (and potentially dangerous).

As an example of this, think of a neighbor you pass regularly in the hall, but haven’t stopped to talk to beyond exchanging brief pleasantries. Even though you don’t know anything substantial about this person, you probably have a positive impression of them—just because you’ve seen them regularly and you’ve never had a bad interaction.

Perceptual Fluency

The perceptual fluency perspective is based on the idea that, when we’ve seen something before, it’s easier for us to understand and interpret it. For example, think about the experience of watching a complex, experimental film. The first time you watch the film, you might find yourself struggling to keep track of what’s happening and who the characters are, and you may not enjoy the movie very much as a result. However, if you watch the movie a second time, the characters and plot will be more familiar to you: psychologists would say that you experienced more perceptual fluency on the second viewing.

According to this perspective, experiencing perceptual fluency puts us in a positive mood. However, we don’t necessarily realize that we’re in a good mood because we’re experiencing fluency: instead, we may simply assume that we’re in a good mood because we liked the thing we just saw. In other words, as a result of experiencing perceptual fluency, we may decide that we liked the movie more on the second viewing.

While psychologists are still debating what causes the mere exposure effect, it seems that having been previously exposed to something can change how we feel about it. And it may explain why, at least sometimes, we tend to prefer things that are already familiar to us.

Sources and Additional Reading

Chenier, Troy & Winkielman, Piotr. “Mere Exposure Effect.” Encyclopedia of Social Psychology . Edited by Roy F. Baumeister and Kathleen D. Vohs, SAGE Publications, 2007, 556-558. http://dx.doi.org/10.4135/9781412956253.n332
Montoya, R. M., Horton, R. S., Vevea, J. L., Citkowicz, M., & Lauber, E. A. (2017). A re-examination of the mere exposure effect: The influence of repeated exposure on recognition, familiarity, and liking. Psychological Bulletin , 143 (5), 459-498. https://psycnet.apa.org/record/2017-10109-001
Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology , 9 (2.2), 1-27. https://psycnet.apa.org/record/1968-12019-001
Zajonc, R. B. (2001). Mere exposure: A gateway to the subliminal. Current Directions in Psychological Science , 10 (6), 224-228. https://doi.org/10.1111/1467-8721.00154
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Facial Feedback Hypothesis (Definition + Examples)

We show our emotions through our facial expressions. We smile when we are happy and frown when we are angry. This is one of the ways we communicate our feelings to others. But did you know it might also work the other way around? Our facial expressions can influence our emotions.

This is the main assumption of the facial feedback hypothesis.

What Is the Facial Feedback Hypothesis?

The facial feedback hypothesis suggests that contractions of the facial muscles communicate our feelings not only to others but also to ourselves. In other words, our facial movements directly influence our emotional state and our mood even if the circumstances around us don't change!

All humans are thought to share seven basic emotions : happiness, surprise, contempt, disgust, sadness, anger, and fear. Each one of these emotions has unique facial expressions associated with it. Raised lip corners and crow’s feet wrinkles around eyes mean joy, while tightened lips and eyebrows pulled down signify contempt.

But facial expressions are more than just representations of our emotions. They contribute to and sustain what we are feeling.

Example of Facial Feedback Hypothesis at Work

The best example of this theory is easy to perform. Go to the mirror and smile. Keep smiling...keep smiling! Even if you were in a bad mood before, you are likely to lighten up and maybe even start laughing! (This is much more fun to try than scowling!)

Who First Wrote About Facial Feedback Hypothesis?

The origins of facial feedback hypothesis can be traced back to the 1870s when Charles Darwin conducted one of the first studies on how we recognize emotion in faces. Darwin suggested that facial expressions of emotions are innate and universal across cultures and societies. In his book The Expression of the Emotions in Man and Animals, he argued that all humans and animals show emotion through similar behaviors.

Paul Ekman's Contributions to Facial Feedback Hypothesis

Numerous studies have since confirmed Darwin’s idea that facial expressions are not socially learned. Instead, they appear to be biological in nature. In the 1950s, American psychologist Paul Ekman did extensive research on facial expressions in different cultures. His findings were in line with Darwin’s idea of universality. Even the members of most remote and isolated tribes portrayed basic emotions using the same facial movements as we do.

What’s more, expressing emotions through facial movements is not any different in people who were born blind. Although they can neither see nor imitate others, they still use the same facial expressions to project their emotions as sighted people do.

There are, however, a few exceptions.

People with schizophrenia and individuals on the autism spectrum have not only difficulty recognizing nonverbal expressions of emotions, but also producing these spontaneous expressions themselves. They typically either remain expressionless or have looks that are hard to interpret.

The James-Lange theory of emotion

A decade after Darwin’s study, the father of American psychology William James and Danish physiologist Carl Lange proposed a new theory of emotion that has served as a basis for the facial feedback hypothesis. The James-Lange Theory of Emotion implies that our facial expressions and other physiological changes create our emotions.

James famously illustrated this assertion with a story of a man being chased by a bear. A man is unfortunate enough to encounter a bear in a forest. He is afraid and, naturally, his heart races and he is sweating as he starts running away. According to the psychologist, it is precisely these physiological changes that provoke the man’s feeling of fear. In other words, he doesn’t run from the bear because he is afraid. He is afraid because of his physiological response to running away.

Fritz Strack’s cartoon experiment

In 1988, German psychologist Fritz Strack and his colleagues conducted a well-known experiment to demonstrate the facial feedback hypothesis. The participants in Strack’s experiment were instructed to look at cartoons and say how funny they thought these cartoons were. They were asked to do this while holding a pen in their mouths. Some participants held the pen with their lips, which pushed the face into a frown-like expression. Others held it with their teeth, forcing a smile.

Strack’s results were in line with the facial feedback hypothesis and were since confirmed by several other studies. The participants who used a pen to mimic a smile thought that the cartoons were funnier than those who were frowning. The participants’ emotions were clearly influenced by their facial expressions.

Characteristics of Facial Feedback

The brain is hardwired to use the facial muscles in specific ways in order to reflect emotions. When contracted, facial muscles pull on the skin allowing us to produce countless expressions ranging from frowning to smiling, raising an eyebrow, and winking. In fact, we are capable of making thousands of different facial expressions, each one lasting anywhere between 0.5 seconds (microexpressions) to 4 seconds.

But facial expressions can indicate various degrees of emotions as well. When we are slightly angry, we display only a light frown and somewhat furrowed eyebrows. If we are furious, our expression becomes more distinctive. In addition, we can show combinations of different emotions through subtle variations of our facial movements.

The facial feedback hypothesis has the strongest effect when it comes to modulation, that is, intensifying our existing feelings rather than initiating a completely new emotion.

Modulating also means that if we avoid showing our emotions using our facial muscles we will, as a consequence, experience a weaker emotional response. We won’t feel the emotions as strongly as we otherwise would. The lack of facial expressions or inhibition of these expressions lead to the suppression of our emotional states.

Applications of the Facial Feedback Hypothesis

The facial feedback phenomenon has several possible applications. It can help us be more positive, have better control of our emotions, and strengthen our feelings of empathy. We can simply use the facial feedback hypothesis to make us feel better in situations that we would rather avoid. If we force a smile instead of frowning at a boring event, for example, we may actually start to enjoy ourselves a bit more. We can use the same exercise whenever we are feeling overwhelmed, powerless, or stressed.

Research shows that regulating emotions through facial feedback can have positive outcomes in areas ranging from psychotherapy to child education and endurance performances.

Paul Ekman Biography - Contributions To Psychology
Body Language Basics - How to Read Someone
Facial Expressions of Emotions (Microexpressions)
James-Lange Theory of Emotion (Definition + Examples)
Two Factor Theory of Emotion

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Buffering Effect

Buffering effect definition.

Buffering Effect History and Modern Usage

The concept of buffering originated from studies on the effects of life stress. Researchers observed that there was considerable variability in individual reactions to major negative events such as illness, unemployment, or bereavement. Some persons were very affected by the events, showing high levels of depression, anxiety, and physical symptoms; but other persons who experienced such events did not show very high levels of symptomatology and recovered more quickly. These observations led to the concept that persons who had certain resources were relatively protected (i.e., buffered) from the adverse impact of life events.

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Buffering effects are demonstrated in studies that include measures of major life events experienced during a certain time frame (e.g., the past year), a proposed resource, and psychological and/or physical symptomatology. Persons who have experienced more negative life events have higher levels of symptomatology, but studies show that life events have less impact (sometimes almost no impact) among persons with high levels of psychosocial resources.

The resource most often studied is social support. Persons who have high levels of social support are less affected by negative life events. Buffering effects have been found for aspects including emotional support (being able to confide in a friend or family member when one is having problems) and instrumental support (being able to obtain goods or services, e.g., money, transportation, child care) that help one to deal with stressful events.

Studies of social support have found buffering effects with mortality as the outcome. Life stress increases mortality over 5- to 10-year periods, but persons with larger social networks, more emotional support, and more participation in community activities have relatively lower rates of mortality under high stress, compared with persons having less social support. Social capital, interpersonal trust, and cohesion at the community level, may also have such an effect.

Social relationships are not the only buffering agent. A personality complex termed hardiness, an orientation toward stressors based on feelings of commitment, control, and challenge, has shown such effects: Persons with a hardy personality show fewer symptoms under high stress. Optimism, the belief that things will generally turn out well, is an outcome expectancy that can produce buffering effects for psychological and physical symptomatology.

Research on buffering has helped to delineate pathways through which life stress may bring on health problems. It has also shown that buffering resources influence how people cope with stressors, leading to procedures for training persons in adaptive coping mechanisms so that effects of negative events can be reduced.

References:

Friedman, H. S. (Ed.). (1990). Personality and disease. New York: Wiley.
Kawachi, I., Kennedy, B., Lochner, K., & Prothrow-Stith, D. (1997). Social capital and mortality. American Journal of Public Health, 87, 1491-1498.
Wills, T. A., & Filer, M. (2001). Social networks and social support. In A. Baum, T. A. Revenson, & J. E. Singer (Eds.), Handbook of health psychology (pp. 209-234). Mahwah, NJ: Erlbaum.

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Catharsis in Psychology

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

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Catharsis Definition

Therapeutic Uses of Catharsis

Catharsis in everyday language, examples of catharsis.

Catharsis is a powerful emotional release that, when successful, is accompanied by cognitive insight and positive change. According to psychoanalytic theory , this emotional release is linked to a need to relieve unconscious conflicts. For example, experiencing stress over a work-related situation may cause feelings of frustration and tension.

Stress , anxiety, fear, anger, and trauma can cause intense and difficult feelings to build over time. At a certain point, it feels as if there is so much emotion and turmoil that it becomes overwhelming. People may even feel as if they are going to "explode" unless they find a way to release this pent-up emotion.

Rather than venting these feelings inappropriately, the individual may instead release these feelings in another way, such as through physical activity or another stress-relieving activity.

The Meaning of Catharsis

The term itself comes from the Greek katharsis meaning "purification" or "cleansing." The term is used in therapy as well as in literature. The hero of a novel might experience an emotional catharsis that leads to some sort of restoration or renewal. The purpose of catharsis is to bring about some form of positive change in the individual's life.

Catharsis involves both a powerful emotional component in which strong feelings are felt and expressed, as well as a cognitive component in which the individual gains new insights.

The term has been in use since the time of the Ancient Greeks, but it was Sigmund Freud's colleague Josef Breuer who was the first to use the term to describe a therapeutic technique. Breuer developed what he referred to as a "cathartic" treatment for hysteria .

His treatment involved having patients recall traumatic experiences while under hypnosis . By consciously expressing emotions that had been long repressed, Breuer found that his patients experienced relief from their symptoms.

Freud also believed that catharsis could play an important role in relieving symptoms of distress.

According to Freud’s psychoanalytic theory, the human mind is composed of three key elements: the conscious, the preconscious, and the unconscious . The conscious mind contains all of the things we are aware of.

The preconscious contains things that we might not be immediately aware of but that we can draw into awareness with some effort or prompting. Finally, the unconscious mind is the part of the mind containing the huge reservoir of thoughts, feelings, and memories that are outside of awareness.

The unconscious mind played a critical role in Freud’s theory . While the contents of the unconscious were out of awareness, he still believed that they continued to exert an influence on behavior and functioning. Freud believed that people could achieve catharsis by bringing these unconscious feelings and memories to light. This process involved using psychotherapeutic tools such as dream interpretation and free association.

In their book Studies on Hysteria , Freud and Breuer defined catharsis as "the process of reducing or eliminating a complex by recalling it to conscious awareness and allowing it to be expressed." Catharsis still plays a role today in Freudian psychoanalysis.

The term catharsis has also found a place in everyday language, often used to describe moments of insight or the experience of finding closure. An individual going through a divorce might describe experiencing a cathartic moment that helps bring them a sense of peace and helps that person move past the bad relationship.

People also describe experiencing catharsis after experiencing some sort of traumatic or stressful event such as a health crisis, job loss, accident, or the death of a loved one. While used somewhat differently than it is traditionally employed in psychoanalysis, the term is still often used to describe an emotional moment that leads to positive change in the person’s life.

Catharsis can take place during the course of therapy, but it can also occur during other moments as well. Some examples of how catharsis might take place include:

Talking with a friend. A discussion with a friend about a problem you are facing might spark a moment of insight in which you are able to see how an event from earlier in your life might be contributing to your current patterns of behavior. This emotional release may help you feel better able to face your current dilemma.
Listening to music. Music can be motivational, but it can also often spark moments of great insight. Music can allow you to release emotions in a way that often leaves you feeling restored.
Creating or viewing art. A powerful artwork can stir deep emotions. Creating art can also be a form of release.
Exercise. The physical demands of exercise can be a great way to work through strong emotions and release them in a constructive manner.
Psychodrama. This type of therapy involves acting out difficult events from the past. By doing so, people are sometimes able to reassess and let go of the pain from these events.
Expressive writing and journaling. Writing can be an effective mental health tool, whether you are journaling or writing fiction. Expressive writing, a process that involves writing about traumatic or stressful events, may be helpful for gaining insight and relieving stressful emotions.
Various therapy approaches. Catharsis plays an important role in emotionally focused , psychodynamic , and primal therapies .

Remember that exploring difficult emotions can come with risks, particularly if these experiences are rooted in trauma or abuse. If you are concerned about the potential effects of exploring these emotions, consider working with a trained mental health professional.

Some researchers also believe that, although catharsis might relieve tension in the short term, it might also serve to reinforce negative behaviors and increase the risk of emotional outbursts in the future.

If you or someone you know is in emotional distress or crisis, the 988 Suicide & Crisis Hotline (formerly the National Suicide Prevention Lifeline) is available 24 hours a day throughout the U.S. Call or text 988, or visit 988lifeline.org .

A Word From Verywell

Catharsis can play a role in helping people deal with difficult or painful emotions. This emotional release can also be an important therapeutic tool for coping with fear, depression, and anxiety. If you are coping with difficult emotions, talking to a mental health professional can help you to explore different techniques that can lead to catharsis.

Sandhu P. Step Aside, Freud: Josef Breuer Is the True Father of Modern Psychotherapy . Scientific American . 2015.

Encyclopeadia Britannica. Sigmund Freud. Psychoanalytic Theory .

Breuer J, Freud S. Studies on Hysteria . Harmondsworth: Penguin Books; 1974.

Sachs ME, Damasio A, Habibi A. The pleasures of sad music: a systematic review . Front Hum Neurosci . 2015;9:404. doi:10.3389/fnhum.2015.00404

Childs E, de Wit H. Regular exercise is associated with emotional resilience to acute stress in healthy adults . Front Physiol . 2014;5:161. doi:10.3389/fphys.2014.00161

Orkibi H, Feniger-Schaal R. Integrative systematic review of psychodrama psychotherapy research: Trends and methodological implications . PLoS One . 2019;14(2):e0212575. doi:10.1371/journal.pone.0212575

Bushman BJ. Does venting anger feed or extinguish the flame? Catharsis, rumination, distraction, anger, and aggressive responding . Pers Soc Psychol Bull . 2002;28(6):724-731. doi:10.1177/0146167202289002

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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Facial Feedback Hypothesis (Definition
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Catharsis in Psychology: Definition, Uses, and Examples
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Aims and Hypotheses

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Facial Feedback Hypothesis (Definition + Examples)

What Is the Facial Feedback Hypothesis?

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Catharsis in Psychology