what's a directional hypothesis in psychology

Directional Hypothesis: Definition and 10 Examples

A directional hypothesis refers to a type of hypothesis used in statistical testing that predicts a particular direction of the expected relationship between two variables.

In simpler terms, a directional hypothesis is an educated, specific guess about the direction of an outcome—whether an increase, decrease, or a proclaimed difference in variable sets.

For example, in a study investigating the effects of sleep deprivation on cognitive performance, a directional hypothesis might state that as sleep deprivation (Independent Variable) increases, cognitive performance (Dependent Variable) decreases (Killgore, 2010). Such a hypothesis offers a clear, directional relationship whereby a specific increase or decrease is anticipated.

Global warming provides another notable example of a directional hypothesis. A researcher might hypothesize that as carbon dioxide (CO2) levels increase, global temperatures also increase (Thompson, 2010). In this instance, the hypothesis clearly articulates an upward trend for both variables. 

In any given circumstance, it’s imperative that a directional hypothesis is grounded on solid evidence. For instance, the CO2 and global temperature relationship is based on substantial scientific evidence, and not on a random guess or mere speculation (Florides & Christodoulides, 2009).

Directional vs Non-Directional vs Null Hypotheses

A directional hypothesis is generally contrasted to a non-directional hypothesis. Here’s how they compare:

• Directional hypothesis: A directional hypothesis provides a perspective of the expected relationship between variables, predicting the direction of that relationship (either positive, negative, or a specific difference). 
• Non-directional hypothesis: A non-directional hypothesis denotes the possibility of a relationship between two variables ( the independent and dependent variables ), although this hypothesis does not venture a prediction as to the direction of this relationship (Ali & Bhaskar, 2016). For example, a non-directional hypothesis might state that there exists a relationship between a person’s diet (independent variable) and their mood (dependent variable), without indicating whether improvement in diet enhances mood positively or negatively. Overall, the choice between a directional or non-directional hypothesis depends on the known or anticipated link between the variables under consideration in research studies.

Another very important type of hypothesis that we need to know about is a null hypothesis :

• Null hypothesis : The null hypothesis stands as a universality—the hypothesis that there is no observed effect in the population under study, meaning there is no association between variables (or that the differences are down to chance). For instance, a null hypothesis could be constructed around the idea that changing diet (independent variable) has no discernible effect on a person’s mood (dependent variable) (Yan & Su, 2016). This proposition is the one that we aim to disprove in an experiment.

While directional and non-directional hypotheses involve some integrated expectations about the outcomes (either distinct direction or a vague relationship), a null hypothesis operates on the premise of negating such relationships or effects.

The null hypotheses is typically proposed to be negated or disproved by statistical tests, paving way for the acceptance of an alternate hypothesis (either directional or non-directional).

Directional Hypothesis Examples

1. exercise and heart health.

Research suggests that as regular physical exercise (independent variable) increases, the risk of heart disease (dependent variable) decreases (Jakicic, Davis, Rogers, King, Marcus, Helsel, Rickman, Wahed, Belle, 2016). In this example, a directional hypothesis anticipates that the more individuals maintain routine workouts, the lesser would be their odds of developing heart-related disorders. This assumption is based on the underlying fact that routine exercise can help reduce harmful cholesterol levels, regulate blood pressure, and bring about overall health benefits. Thus, a direction – a decrease in heart disease – is expected in relation with an increase in exercise. 

2. Screen Time and Sleep Quality

Another classic instance of a directional hypothesis can be seen in the relationship between the independent variable, screen time (especially before bed), and the dependent variable, sleep quality. This hypothesis predicts that as screen time before bed increases, sleep quality decreases (Chang, Aeschbach, Duffy, Czeisler, 2015). The reasoning behind this hypothesis is the disruptive effect of artificial light (especially blue light from screens) on melatonin production, a hormone needed to regulate sleep. As individuals spend more time exposed to screens before bed, it is predictably hypothesized that their sleep quality worsens. 

3. Job Satisfaction and Employee Turnover

A typical scenario in organizational behavior research posits that as job satisfaction (independent variable) increases, the rate of employee turnover (dependent variable) decreases (Cheng, Jiang, & Riley, 2017). This directional hypothesis emphasizes that an increased level of job satisfaction would lead to a reduced rate of employees leaving the company. The theoretical basis for this hypothesis is that satisfied employees often tend to be more committed to the organization and are less likely to seek employment elsewhere, thus reducing turnover rates.

4. Healthy Eating and Body Weight

Healthy eating, as the independent variable, is commonly thought to influence body weight, the dependent variable, in a positive way. For example, the hypothesis might state that as consumption of healthy foods increases, an individual’s body weight decreases (Framson, Kristal, Schenk, Littman, Zeliadt, & Benitez, 2009). This projection is based on the premise that healthier foods, such as fruits and vegetables, are generally lower in calories than junk food, assisting in weight management.

5. Sun Exposure and Skin Health

The association between sun exposure (independent variable) and skin health (dependent variable) allows for a definitive hypothesis declaring that as sun exposure increases, the risk of skin damage or skin cancer increases (Whiteman, Whiteman, & Green, 2001). The premise aligns with the understanding that overexposure to the sun’s ultraviolet rays can deteriorate skin health, leading to conditions like sunburn or, in extreme cases, skin cancer.

6. Study Hours and Academic Performance

A regularly assessed relationship in academia suggests that as the number of study hours (independent variable) rises, so too does academic performance (dependent variable) (Nonis, Hudson, Logan, Ford, 2013). The hypothesis proposes a positive correlation , with an increase in study time expected to contribute to enhanced academic outcomes.

7. Screen Time and Eye Strain

It’s commonly hypothesized that as screen time (independent variable) increases, the likelihood of experiencing eye strain (dependent variable) also increases (Sheppard & Wolffsohn, 2018). This is based on the idea that prolonged engagement with digital screens—computers, tablets, or mobile phones—can cause discomfort or fatigue in the eyes, attributing to symptoms of eye strain.

8. Physical Activity and Stress Levels

In the sphere of mental health, it’s often proposed that as physical activity (independent variable) increases, levels of stress (dependent variable) decrease (Stonerock, Hoffman, Smith, Blumenthal, 2015). Regular exercise is known to stimulate the production of endorphins, the body’s natural mood elevators, helping to alleviate stress.

9. Water Consumption and Kidney Health

A common health-related hypothesis might predict that as water consumption (independent variable) increases, the risk of kidney stones (dependent variable) decreases (Curhan, Willett, Knight, & Stampfer, 2004). Here, an increase in water intake is inferred to reduce the risk of kidney stones by diluting the substances that lead to stone formation.

10. Traffic Noise and Sleep Quality

In urban planning research, it’s often supposed that as traffic noise (independent variable) increases, sleep quality (dependent variable) decreases (Muzet, 2007). Increased noise levels, particularly during the night, can result in sleep disruptions, thus, leading to poor sleep quality.

11. Sugar Consumption and Dental Health

In the field of dental health, an example might be stating as one’s sugar consumption (independent variable) increases, dental health (dependent variable) decreases (Sheiham, & James, 2014). This stems from the fact that sugar is a major factor in tooth decay, and increased consumption of sugary foods or drinks leads to a decline in dental health due to the high likelihood of cavities.

See 15 More Examples of Hypotheses Here

A directional hypothesis plays a critical role in research, paving the way for specific predicted outcomes based on the relationship between two variables. These hypotheses clearly illuminate the expected direction—the increase or decrease—of an effect. From predicting the impacts of healthy eating on body weight to forecasting the influence of screen time on sleep quality, directional hypotheses allow for targeted and strategic examination of phenomena. In essence, directional hypotheses provide the crucial path for inquiry, shaping the trajectory of research studies and ultimately aiding in the generation of insightful, relevant findings.

Ali, S., & Bhaskar, S. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662-669. doi: https://doi.org/10.4103%2F0019-5049.190623  

Chang, A. M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proceeding of the National Academy of Sciences, 112 (4), 1232-1237. doi: https://doi.org/10.1073/pnas.1418490112  

Cheng, G. H. L., Jiang, D., & Riley, J. H. (2017). Organizational commitment and intrinsic motivation of regular and contractual primary school teachers in China. New Psychology, 19 (3), 316-326. Doi: https://doi.org/10.4103%2F2249-4863.184631  

Curhan, G. C., Willett, W. C., Knight, E. L., & Stampfer, M. J. (2004). Dietary factors and the risk of incident kidney stones in younger women: Nurses’ Health Study II. Archives of Internal Medicine, 164 (8), 885–891.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment international , 35 (2), 390-401. doi: https://doi.org/10.1016/j.envint.2008.07.007

Framson, C., Kristal, A. R., Schenk, J. M., Littman, A. J., Zeliadt, S., & Benitez, D. (2009). Development and validation of the mindful eating questionnaire. Journal of the American Dietetic Association, 109 (8), 1439-1444. doi: https://doi.org/10.1016/j.jada.2009.05.006  

Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., … & Belle, S. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316 (11), 1161-1171.

Khan, S., & Iqbal, N. (2013). Study of the relationship between study habits and academic achievement of students: A case of SPSS model. Higher Education Studies, 3 (1), 14-26.

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Progress in brain research , 185 , 105-129. doi: https://doi.org/10.1016/B978-0-444-53702-7.00007-5  

Marczinski, C. A., & Fillmore, M. T. (2014). Dissociative antagonistic effects of caffeine on alcohol-induced impairment of behavioral control. Experimental and Clinical Psychopharmacology, 22 (4), 298–311. doi: https://psycnet.apa.org/doi/10.1037/1064-1297.11.3.228  

Muzet, A. (2007). Environmental Noise, Sleep and Health. Sleep Medicine Reviews, 11 (2), 135-142. doi: https://doi.org/10.1016/j.smrv.2006.09.001  

Nonis, S. A., Hudson, G. I., Logan, L. B., & Ford, C. W. (2013). Influence of perceived control over time on college students’ stress and stress-related outcomes. Research in Higher Education, 54 (5), 536-552. doi: https://doi.org/10.1023/A:1018753706925  

Sheiham, A., & James, W. P. (2014). A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public health nutrition, 17 (10), 2176-2184. Doi: https://doi.org/10.1017/S136898001400113X  

Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology , 3 (1), e000146. doi: http://dx.doi.org/10.1136/bmjophth-2018-000146

Stonerock, G. L., Hoffman, B. M., Smith, P. J., & Blumenthal, J. A. (2015). Exercise as Treatment for Anxiety: Systematic Review and Analysis. Annals of Behavioral Medicine, 49 (4), 542–556. doi: https://doi.org/10.1007/s12160-014-9685-9  

Thompson, L. G. (2010). Climate change: The evidence and our options. The Behavior Analyst , 33 , 153-170. Doi: https://doi.org/10.1007/BF03392211  

Whiteman, D. C., Whiteman, C. A., & Green, A. C. (2001). Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes & Control, 12 (1), 69-82. doi: https://doi.org/10.1023/A:1008980919928

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing . New Jersey: World Scientific.

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Directional Hypothesis

Definition:

A directional hypothesis is a specific type of hypothesis statement in which the researcher predicts the direction or effect of the relationship between two variables.

Key Features

1. Predicts direction:

Unlike a non-directional hypothesis, which simply states that there is a relationship between two variables, a directional hypothesis specifies the expected direction of the relationship.

2. Involves one-tailed test:

Directional hypotheses typically require a one-tailed statistical test, as they are concerned with whether the relationship is positive or negative, rather than simply whether a relationship exists.

3. Example:

An example of a directional hypothesis would be: “Increasing levels of exercise will result in greater weight loss.”

4. Researcher’s prior belief:

A directional hypothesis is often formed based on the researcher’s prior knowledge, theoretical understanding, or previous empirical evidence relating to the variables under investigation.

5. Confirmatory nature:

Directional hypotheses are considered confirmatory, as they provide a specific prediction that can be tested statistically, allowing researchers to either support or reject the hypothesis.

6. Advantages and disadvantages:

Directional hypotheses help focus the research by explicitly stating the expected relationship, but they can also limit exploration of alternative explanations or unexpected findings.

Statistics Made Easy

What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis:

• Directional hypothesis: The alternative hypothesis contains the less than (“<“) or greater than (“>”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
• Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

• H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
• H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

• H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)
• H A : μ < 10 inches (the pesticide will cause mean plant growth to decrease)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “<” sign. The biologist believes that the pesticide will influence the mean plant growth in a negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

• H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
• H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

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Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, alternative to the null.

Directional hypothesis: A directional (or one tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

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Aims And Hypotheses, Directional And Non-Directional

March 7, 2021 - paper 2 psychology in context | research methods.

• Back to Paper 2 - Research Methods

In Psychology, hypotheses are predictions made by the researcher about the outcome of a study. The research can chose to make a specific prediction about what they feel will happen in their research (a directional hypothesis) or they can make a ‘general,’ ‘less specific’ prediction about the outcome of their research (a non-directional hypothesis). The type of prediction that a researcher makes is usually dependent on whether or not any previous research has also investigated their research aim.

Variables Recap:

The  independent variable  (IV)  is the variable that psychologists  manipulate/change  to see if changing this variable has an effect on the  depen dent variable  (DV).

The  dependent variable (DV)  is the variable that the psychologists  measures  (to see if the IV has had an effect).

It is important that the only variable that is changed in research is the  independent variable (IV),   all other variables have to be kept constant across the control condition and the experimental conditions. Only then will researchers be able to observe the true effects of  just  the independent variable (IV) on the dependent variable (DV).

Research/Experimental Aim(S):

An aim is a clear and precise statement of the purpose of the study. It is a statement of why a research study is taking place. This should include what is being studied and what the study is trying to achieve. (e.g. “This study aims to investigate the effects of alcohol on reaction times”.

It is important that aims created in research are realistic and ethical.

Hypotheses:

This is a testable statement that predicts what the researcher expects to happen in their research. The research study itself is therefore a means of testing whether or not the hypothesis is supported by the findings. If the findings do support the hypothesis then the hypothesis can be retained (i.e., accepted), but if not, then it must be rejected.

Three Different Hypotheses:

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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.

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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Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

Customer support manager

In the world of research and statistical analysis, hypotheses play a crucial role in formulating and testing scientific claims. Understanding the differences between directional and non-directional hypothesis is essential for designing sound experiments and drawing accurate conclusions. Whether you’re a student, researcher, or simply curious about the foundations of hypothesis testing, this guide will equip you with the knowledge and tools to navigate this fundamental aspect of scientific inquiry.

Understanding Directional Hypothesis

Understanding directional hypotheses is crucial for conducting hypothesis-driven research, as they guide the selection of appropriate statistical tests and aid in the interpretation of results. By incorporating directional hypotheses, researchers can make more precise predictions, contribute to scientific knowledge, and advance their fields of study.

Definition of directional hypothesis

Directional hypotheses, also known as one-tailed hypotheses, are statements in research that make specific predictions about the direction of a relationship or difference between variables. Unlike non-directional hypotheses, which simply state that there is a relationship or difference without specifying its direction, directional hypotheses provide a focused and precise expectation.

A directional hypothesis predicts either a positive or negative relationship between variables or predicts that one group will perform better than another. It asserts a specific direction of effect or outcome. For example, a directional hypothesis could state that “increased exposure to sunlight will lead to an improvement in mood” or “participants who receive the experimental treatment will exhibit higher levels of cognitive performance compared to the control group.”

Directional hypotheses are formulated based on existing theory, prior research, or logical reasoning, and they guide the researcher’s expectations and analysis. They allow for more targeted predictions and enable researchers to test specific hypotheses using appropriate statistical tests.

The role of directional hypothesis in research

Directional hypotheses also play a significant role in research surveys. Let’s explore their role specifically in the context of survey research:

• Objective-driven surveys : Directional hypotheses help align survey research with specific objectives. By formulating directional hypotheses, researchers can focus on gathering data that directly addresses the predicted relationship or difference between variables of interest.
• Question design and measurement : Directional hypotheses guide the design of survey question types and the selection of appropriate measurement scales. They ensure that the questions are tailored to capture the specific aspects related to the predicted direction, enabling researchers to obtain more targeted and relevant data from survey respondents.
• Data analysis and interpretation : Directional hypotheses assist in data analysis by directing researchers towards appropriate statistical tests and methods. Researchers can analyze the survey data to specifically test the predicted relationship or difference, enhancing the accuracy and reliability of their findings. The results can then be interpreted within the context of the directional hypothesis, providing more meaningful insights.
• Practical implications and decision-making : Directional hypotheses in surveys often have practical implications. When the predicted relationship or difference is confirmed, it informs decision-making processes, program development, or interventions. The survey findings based on directional hypotheses can guide organizations, policymakers, or practitioners in making informed choices to achieve desired outcomes.
• Replication and further research : Directional hypotheses in survey research contribute to the replication and extension of studies. Researchers can replicate the survey with different populations or contexts to assess the generalizability of the predicted relationships. Furthermore, if the directional hypothesis is supported, it encourages further research to explore underlying mechanisms or boundary conditions.

By incorporating directional hypotheses in survey research, researchers can align their objectives, design effective surveys, conduct focused data analysis, and derive practical insights. They provide a framework for organizing survey research and contribute to the accumulation of knowledge in the field.

Examples of research questions for directional hypothesis

Here are some examples of research questions that lend themselves to directional hypotheses:

• Does increased daily exercise lead to a decrease in body weight among sedentary adults?
• Is there a positive relationship between study hours and academic performance among college students?
• Does exposure to violent video games result in an increase in aggressive behavior among adolescents?
• Does the implementation of a mindfulness-based intervention lead to a reduction in stress levels among working professionals?
• Is there a difference in customer satisfaction between Product A and Product B, with Product A expected to have higher satisfaction ratings?
• Does the use of social media influence self-esteem levels, with higher social media usage associated with lower self-esteem?
• Is there a negative relationship between job satisfaction and employee turnover, indicating that lower job satisfaction leads to higher turnover rates?
• Does the administration of a specific medication result in a decrease in symptoms among individuals with a particular medical condition?
• Does increased access to early childhood education lead to improved cognitive development in preschool-aged children?
• Is there a difference in purchase intention between advertisements with celebrity endorsements and advertisements without, with celebrity endorsements expected to have a higher impact?

These research questions generate specific predictions about the direction of the relationship or difference between variables and can be tested using appropriate research methods and statistical analyses.

Definition of non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, are statements in research that indicate the presence of a relationship or difference between variables without specifying the direction of the effect. Instead of making predictions about the specific direction of the relationship or difference, non-directional hypotheses simply state that there is an association or distinction between the variables of interest.

Non-directional hypotheses are often used when there is no prior theoretical basis or clear expectation about the direction of the relationship. They leave the possibility open for either a positive or negative relationship, or for both groups to differ in some way without specifying which group will perform better or worse.

Advantages and utility of non-directional hypothesis

Non-directional hypotheses in survey s offer several advantages and utilities, providing flexibility and comprehensive analysis of survey data. Here are some of the key advantages and utilities of using non-directional hypotheses in surveys:

• Exploration of Relationships : Non-directional hypotheses allow researchers to explore and examine relationships between variables without assuming a specific direction. This is particularly useful in surveys where the relationship between variables may not be well-known or there may be conflicting evidence regarding the direction of the effect.
• Flexibility in Question Design : With non-directional hypotheses, survey questions can be designed to measure the relationship between variables without being biased towards a particular outcome. This flexibility allows researchers to collect data and analyze the results more objectively.
• Open to Unexpected Findings : Non-directional hypotheses enable researchers to be open to unexpected or surprising findings in survey data. By not committing to a specific direction of the effect, researchers can identify and explore relationships that may not have been initially anticipated, leading to new insights and discoveries.
• Comprehensive Analysis : Non-directional hypotheses promote comprehensive analysis of survey data by considering the possibility of an effect in either direction. Researchers can assess the magnitude and significance of relationships without limiting their analysis to only one possible outcome.
• S tatistical Validity : Non-directional hypotheses in surveys allow for the use of two-tailed statistical tests, which provide a more conservative and robust assessment of significance. Two-tailed tests consider both positive and negative deviations from the null hypothesis, ensuring accurate and reliable statistical analysis of survey data.
• Exploratory Research : Non-directional hypotheses are particularly useful in exploratory research, where the goal is to gather initial insights and generate hypotheses. Surveys with non-directional hypotheses can help researchers explore various relationships and identify patterns that can guide further research or hypothesis development.

It is worth noting that the choice between directional and non-directional hypotheses in surveys depends on the research objectives, existing knowledge, and the specific variables being investigated. Researchers should carefully consider the advantages and limitations of each approach and select the one that aligns best with their research goals and survey design.

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What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis:

• Directional hypothesis: The alternative hypothesis contains the less than (“”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
• Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

• H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
• H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

• H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

• H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
• H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

How to Perform a Partial F-Test in Excel

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DIRECTIONAL HYPOTHESIS

Prediction relating to the direction of experimental scores from one group will differ to another group.

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What is The Null Hypothesis & When Do You Reject The Null Hypothesis

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

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

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A null hypothesis is a statistical concept suggesting no significant difference or relationship between measured variables. It’s the default assumption unless empirical evidence proves otherwise.

The null hypothesis states no relationship exists between the two variables being studied (i.e., one variable does not affect the other).

The null hypothesis is the statement that a researcher or an investigator wants to disprove.

Testing the null hypothesis can tell you whether your results are due to the effects of manipulating ​ the dependent variable or due to random chance. 

How to Write a Null Hypothesis

Null hypotheses (H0) start as research questions that the investigator rephrases as statements indicating no effect or relationship between the independent and dependent variables.

It is a default position that your research aims to challenge or confirm.

For example, if studying the impact of exercise on weight loss, your null hypothesis might be:

There is no significant difference in weight loss between individuals who exercise daily and those who do not.

Examples of Null Hypotheses

When do we reject the null hypothesis .

We reject the null hypothesis when the data provide strong enough evidence to conclude that it is likely incorrect. This often occurs when the p-value (probability of observing the data given the null hypothesis is true) is below a predetermined significance level.

If the collected data does not meet the expectation of the null hypothesis, a researcher can conclude that the data lacks sufficient evidence to back up the null hypothesis, and thus the null hypothesis is rejected. 

Rejecting the null hypothesis means that a relationship does exist between a set of variables and the effect is statistically significant ( p > 0.05).

If the data collected from the random sample is not statistically significance , then the null hypothesis will be accepted, and the researchers can conclude that there is no relationship between the variables. 

You need to perform a statistical test on your data in order to evaluate how consistent it is with the null hypothesis. A p-value is one statistical measurement used to validate a hypothesis against observed data.

Calculating the p-value is a critical part of null-hypothesis significance testing because it quantifies how strongly the sample data contradicts the null hypothesis.

The level of statistical significance is often expressed as a  p  -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01) as general guidelines to decide if you should reject or keep the null.

When your p-value is less than or equal to your significance level, you reject the null hypothesis.

In other words, smaller p-values are taken as stronger evidence against the null hypothesis. Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis.

In this case, the sample data provides insufficient data to conclude that the effect exists in the population.

Because you can never know with complete certainty whether there is an effect in the population, your inferences about a population will sometimes be incorrect.

When you incorrectly reject the null hypothesis, it’s called a type I error. When you incorrectly fail to reject it, it’s called a type II error.

Why Do We Never Accept The Null Hypothesis?

The reason we do not say “accept the null” is because we are always assuming the null hypothesis is true and then conducting a study to see if there is evidence against it. And, even if we don’t find evidence against it, a null hypothesis is not accepted.

A lack of evidence only means that you haven’t proven that something exists. It does not prove that something doesn’t exist. 

It is risky to conclude that the null hypothesis is true merely because we did not find evidence to reject it. It is always possible that researchers elsewhere have disproved the null hypothesis, so we cannot accept it as true, but instead, we state that we failed to reject the null. 

One can either reject the null hypothesis, or fail to reject it, but can never accept it.

Why Do We Use The Null Hypothesis?

We can never prove with 100% certainty that a hypothesis is true; We can only collect evidence that supports a theory. However, testing a hypothesis can set the stage for rejecting or accepting this hypothesis within a certain confidence level.

The null hypothesis is useful because it can tell us whether the results of our study are due to random chance or the manipulation of a variable (with a certain level of confidence).

A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis.

Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists. 

Hypothesis testing is a critical part of the scientific method as it helps decide whether the results of a research study support a particular theory about a given population. Hypothesis testing is a systematic way of backing up researchers’ predictions with statistical analysis.

It helps provide sufficient statistical evidence that either favors or rejects a certain hypothesis about the population parameter. 

Purpose of a Null Hypothesis 

• The primary purpose of the null hypothesis is to disprove an assumption. 
• Whether rejected or accepted, the null hypothesis can help further progress a theory in many scientific cases.
• A null hypothesis can be used to ascertain how consistent the outcomes of multiple studies are.

Do you always need both a Null Hypothesis and an Alternative Hypothesis?

The null (H0) and alternative (Ha or H1) hypotheses are two competing claims that describe the effect of the independent variable on the dependent variable. They are mutually exclusive, which means that only one of the two hypotheses can be true. 

While the null hypothesis states that there is no effect in the population, an alternative hypothesis states that there is statistical significance between two variables. 

The goal of hypothesis testing is to make inferences about a population based on a sample. In order to undertake hypothesis testing, you must express your research hypothesis as a null and alternative hypothesis. Both hypotheses are required to cover every possible outcome of the study. 

What is the difference between a null hypothesis and an alternative hypothesis?

The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population.

It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.

What are some problems with the null hypothesis?

One major problem with the null hypothesis is that researchers typically will assume that accepting the null is a failure of the experiment. However, accepting or rejecting any hypothesis is a positive result. Even if the null is not refuted, the researchers will still learn something new.

Why can a null hypothesis not be accepted?

We can either reject or fail to reject a null hypothesis, but never accept it. If your test fails to detect an effect, this is not proof that the effect doesn’t exist. It just means that your sample did not have enough evidence to conclude that it exists.

We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.

Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.

If the p-value is greater than the significance level, then you fail to reject the null hypothesis.

Is a null hypothesis directional or non-directional?

A hypothesis test can either contain an alternative directional hypothesis or a non-directional alternative hypothesis. A directional hypothesis is one that contains the less than (“<“) or greater than (“>”) sign.

A nondirectional hypothesis contains the not equal sign (“≠”).  However, a null hypothesis is neither directional nor non-directional.

A null hypothesis is a prediction that there will be no change, relationship, or difference between two variables.

The directional hypothesis or nondirectional hypothesis would then be considered alternative hypotheses to the null hypothesis.

Gill, J. (1999). The insignificance of null hypothesis significance testing.  Political research quarterly ,  52 (3), 647-674.

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method.  American Psychologist ,  56 (1), 16.

Masson, M. E. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing.  Behavior research methods ,  43 , 679-690.

Nickerson, R. S. (2000). Null hypothesis significance testing: a review of an old and continuing controversy.  Psychological methods ,  5 (2), 241.

Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test.  Psychological bulletin ,  57 (5), 416.

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• Open access
• Published: 23 April 2024

People are surprisingly hesitant to reach out to old friends

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

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

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

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Introduction.

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

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

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

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

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

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

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

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

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

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

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

Participants

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Manipulation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reporting summary

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Limitations

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

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

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

Implications

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

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

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

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

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

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The Brain Reacts to Racism by Experiencing Pain

What interventions could offset racist bullying.

Posted April 25, 2024 | Reviewed by Kaja Perina

• Neurobiological research shows the pain infused reaction brains have to racism.
• Racism's repeated blows to neurobiology come from individuals, employers, culture, politics, and society.
• Chronic pain is unbearable and one way to lessen it is through mechanisms that lessen racist impulses.

Racism is an intense and extensive form of bullying with an appalling history attached to it, and it continues today as one of the most insidious and damaging forms of bullying behavior. Like all bullying, racism uses power and privilege. It strives to dominate and take over resources. It justifies itself based on beliefs, not facts. It benefits the in-group and strips the out-group of their rights and even their humanity. The beneficiaries might not act, but they remain bystanders who gain protection, who align with power, and who maintain esteem and status regardless of the injustice or suffering of those targeted.

Targets of bullying – whether it’s due to racism or any other impulse to harm – are put in an out-group, objectified, dehumanized, and considered not deserving of empathy according to perpetrators and the systems that all too often enable them. Extensive neurobiological research shows that this repeat rejection and social exclusion causes chronic pain .

Social and Physical Pain Are Bi-Directional

In my book The Bullied Brain , the catalyst for studying research on the way in which abusive behavior impacts the brain was misogyny and homophobia. Extensive research documents the way in which exposure to this kind of bullying causes social and physical pain, which are inseparable from a brain perspective. Likewise, extensive research reveals the way in which exposure to racist bullying causes social and physical pain, which are inseparable from a brain perspective.

For racism to cause targets to live with chronic pain, it must be active. It requires beliefs, words, actions, interventions, and interruptions ranging from micro-aggressions to full-on violations that all amount to layers and layers of pain. Bullying and abuse focus on individual or institutional behaviors. Racism is far more severe in that it is not only experienced on the individual and institutional levels, but it can also be on a cultural level (media, entertainment, art, and literature), as well as a collective level ( education , employment, health, and treatment in the criminal justice system), and it can involve sociopolitical racism (political decisions and legislative processes).

In order to relieve this individual, collective, and social pain and alleviate it, there needs to be at the very minimum an equal response. When we look at the impact of racist bullying through a neurobiological lens, and especially understand how it’s physically harming the brain and causing chronic pain, then diversity, equity, and inclusion (DEI) are not only attempts to establish some kind of reparative justice, they are pain-killers. They are medicine.

If you’ve ever crawled into bed, drapes drawn, with a migraine , suffered the agony of a broken bone, or thought you might lose your mind due to the dull aching of an infected tooth, you know the desperate quest for something, anything, to relieve the pain. Now imagine that this pain was a daily occurrence and when you sought help from medical experts, it was misunderstood and made worse rather than lessened.

Racism’s Impact on the Brain

In 2022, in the article “The Neurobiology of Social Stress Resulting from Racism,” three psychologists and a sociologist reviewed extensive studies over the past fifty years that document the way “negative emotions can cause a human being to experience pain irrespective of the presence of actual or potential tissue damage” and apply this scientific insight to racist experiences. The researchers note that in recent years, it has become clear that the “experience of pain and negative emotions reinforce each other in humans and involve activation of the same brain structures.”

More specifically, the writers cite recent research showing that “experiences of racial discrimination activate many of the same brain regions that are implicated in the affective-motivational processing (i.e., unpleasantness) of physical pain, and more recently, also the sensory-discriminative processing (i.e., intensity) of physical pain.” They draw on extensive neurobiological research to chart the way in which racial discrimination is “associated with increased clinical pain severity, increased experimental pain sensitivity, and increased pain catastrophizing ” among targets.

In 2020, they noted that the International Association for the Study of Pain (IASP) updated the definition of pain to reflect “an unpleasant sensory and emotional experience that is associated with, or resembling that associated with, actual or potential tissue damage.” This definition is critical for all forms of bullying including racism. The key word is “potential.” If someone strikes a body, it suffers tissue damage and results in pain. If someone threatens a brain, a nervous system , a body with social threat, then the reaction of pain is connected to “actual or potential interpersonal rejection or intentional abuse.”

The Agony of Social Rejection

Extensively researched in neuroscience and neurobiology, human mammals are social creatures and the threat of social rejection and interpersonal abuse is as agonizing as physical threats. In “The Neurobiology of Social Stress Resulting from Racism,” the researchers discuss how our painful reaction to social threat is a “protective/adaptive response” because our survival hinges on belonging. Likewise, pain in response to tissue damage is also a survival mechanism that alerts us to danger.

Tissue damage may take weeks to heal and weeks for the pain to subside. Pain from the threat of being rejected, being ostracized, being humiliated, being blocked repeatedly from opportunities and connection, or being verbally and psychologically abused, can go on for much longer and do far greater damage to brain and body as extensively documented in research and covered in The Bullied Brain .

The “Neurobiology of Social Stress” article looks at studies on how even “medical professionals can have false beliefs about biological differences” between black, Hispanic, and white individuals and their beliefs “influence treatment for pain conditions.” Imagine going to seek pain medication and finding yourself in an ongoing socially threatening situation that due to racism worsens your pain.

Diversity, equity, and inclusion are one way to offer "pain medicine" to those who are suffering from the chronic pain of racism. We need to learn from brain science that restorative justice approaches not only work to right past wrongs, but are also a step towards taking the pain away from those wronged.

Fraser, J. (2022). The Bullied Brain . Lanham, MA.: Prometheus Books.

Hobson, J., Moody, M., Sorge, R., & Goodin, B. (2022). “The Neurobiology of Social Stress Resulting from Racism: Implications for Pain Disparities among Racialized Minorities.” Neurobiology of Pain 12.

Jennifer Fraser, Ph.D., is an award-winning educator and bestselling author. Her latest book, The Bullied Brain: Heal Your Scars and Restore Your Health , hit shelves and airwaves in April 2022.

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Non-Directional Hypothesis

A non-directional hypothesis is a two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

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Research Methods: MCQ Revision Test 1 for AQA A Level Psychology

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