21 Extraneous Variables Examples

21 Extraneous Variables ExamplesReviewed by Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

extraneous variable examples and definition, explained below

Extraneous variables (EVs) are factors related to the phenomenon under study, but not specifically included in the research.

It is a third major type of variable in a study:

  1. Independent variable: The variable that is manipulated or changed by the researcher.
  2. Dependent variable: The variable that changes as a result of changes to the independent variable (see also: independent vs dependent variables).
  3. Extraneous Variables: Additional variables that are not intentionally observed or manipulated.

Extraneous variables may inadvertently affect the dependent variable. We don’t want this as it interferes with the internal validity of the study!

For this reason, researchers try to design their studies to minimize the influence of extraneous variables.

Examples of Extraneous Variables

  • Weather: The weather is one very common extraneous variable that can affect results. For example, if you’re testing driving skills between different groups of people on different days, rain on the road will have a huge impact.
  • Sleep: If you’re testing different groups of people’s skills, all the groups need to have the same amount of sleep to minimize this extraneous variable.
  • Participant Motivation: A poorly motivated cohort may do worse in the results, even if motivation isn’t a variable considered by the researchers (see also: cohort effect).
  • External Noise: If two test groups are sent to a study hall to do a test, both groups need to be doing the test with the same levels of external noise so the results aren’t affected.
  • Time of Day: If you tested one group in the morning and one in the afternoon, the results may vary.
  • Demand Characteristics: Demand characteristics occur when research participants know what the study is testing, so they change their behavior to meet the expectations of the researchers.
  • Researcher Bias: A study may be affected by the biases of the researchers conducting the study.
  • Instrumentation Variables: Sometimes, the instruments in a study are not properly calibrated or even change in the middle of the study which muddy the results.
  • Dietary Intake: If you’re examining cognitive performance between groups, it’s crucial to consider their dietary intake. A group that just had a heavy lunch might perform differently than one that’s fasting.
  • Caffeine Consumption: When examining alertness or reaction time, the amount of caffeine ingested by participants can heavily influence the results.
  • Physical Activity: If you’re testing cognitive or physical skills, the amount of physical activity prior to the test can act as an extraneous variable.
  • Emotional State: Participants experiencing heightened emotional states like anxiety or excitement might behave differently in experiments compared to those who are calm.
  • Previous Experience: If one group has had exposure to a similar test or situation in the past, their performance may differ due to their previous experience.
  • Room Temperature: Extreme cold or heat can affect the participant’s comfort and concentration, potentially affecting the study results.
  • Cultural Background: People from different cultural backgrounds may interpret or react to situations differently, which can inadvertently affect results.
  • Location: Different test locations can introduce variables such as air quality, lighting conditions, or general ambiance which can influence results.
  • Age: Age can affect cognitive function, physical ability, and many other factors, so it’s critical to ensure age doesn’t unintentionally influence study results.
  • Medications: Any drugs or medications taken by the participant can affect behavior, cognitive function, or physical responses.
  • Expectancy Effect: Similar to demand characteristics, if participants have certain expectations about the outcomes, it can influence how they respond.
  • Test Duration: The length of time a test takes can affect fatigue levels, attention spans, and performance, making it an important variable to control.
  • Health Conditions: Participants’ current health or any chronic conditions can play a role in their responses or performance in a study.

More Extraneous Variables in Research Studies

1. Sleep Quality and Driving Ability

Independent Variable: Sleep

Dependent Variable: Driving Ability

Extraneous Variable: Road Conditions (Rain)

A team of sleep researchers are interested in how sleep deprivation effects road safety. Their hypothesis is that poor sleeping habits are behind the upward trend of car accidents.

They design a study that involves having participants spend the night in their sleep lab. Half of the participants go to the lab on a Wednesday and are allowed to sleep quietly. The next morning, they take a standardized driving test.

The other half of the participants arrive on a Thursday and are woken-up every time their brain approaches the deep-sleep stage. The next morning, they take the same driving test as the first group of participants.

Each person’s score on the driving test is tabulated and analyzed. The results indicate that the sleep-deprived participants actually performed better on the course than the other participants.

However, it turns out that it rained on the day the ‘sleeping’ participants took through the course, it rained, which impaired their driving ability. In this case, rain is an extraneous variable that affected the DV.   

Group (Independent Variable)Road Conditions (Extraneous Variable)Driving Ability (Dependent Variable)
Drivers with a Good Night’s SleepRainyWorse Scores in Driving Test
Drivers with a Bad Night’s SleepClearBetter Scores in Driving Test

2. Effects of Music on Weight-lifting

Independent Variable: Music

Dependent Variable: Weight-Lifting Ability

Extraneous Variable: Underlying Fitness Levels

Music is a very powerful stimulus. Many people like to listen to music when they are engaged in an athletic activity, such as running or lifting weights.

A sports psychologist wants to know if soft piano music will affect performance at the gym. The study design is fairly straightforward.

As members enter the gym, they are given a set of special headphones that have been preprogrammed to play either piano music or nothing at all.

In this example, there are many extraneous variables:

  • Fitness: the pre-existing physical condition of the gym members can affect their performance, regardless of type of music listened to.
  • Motivation: some members may also differ from each other in terms of their level of motivation.
  • Quality of Sleep: How well they slept the previous night may affect their abilities.

Any variable that could affect the dependent variable is an extraneous variable that can cloud the validity of the results.  

3. Reading Comprehension Study  

Independent Variable: Style of history test

Dependent Variable: Reading comprehension

Extraneous Variable: Noise

Researchers are interested in how the organization of text information affects reading comprehension of primary school students. So, they construct different versions of the same history chapter.

In one version, the text is presented in long form. All the sentences follow sequentially on each page.

In the other version, the text is organized in small blocks of information laid out on the page in different sections, with photos and detailed captions of key events.

The researchers then go to a nearby school and have students in different classroom read one version or the other. Afterwards, the students are all given the same test for comprehension.

Unfortunately, some of the classrooms were located next to the playground. While those students were reading, several classes were having recess, so the environment was quite noisy.

This is an example of an extraneous variable that can definitely create problems for the study’s internal validity.

4. Marital Communication and Stress

Independent Variable: Counselling intervention

Dependent Variable: Marital communication

Extraneous Variable: Christmas holidays

A group of marriage counselors are interested in testing a new method of helping couples communicate.

The method involves having each person in the marriage take turns picking up a topic card and talking about it for 2 minutes. The topics on the cards are about fairly common areas of disagreement among married couples.

The study takes place over a period of 3 months, from early November to the end of January.

When analyzing the results at the end of the study, the counselors notice that couples in the middle of the study seemed to have more arguments than those that participated in the beginning or end.

Can you identify the extraneous variable?

As it turns out, couples in the middle of the study were feeling stressed because of the holiday season that occurs in December. Buying gifts for people and dealing with crowds while shopping made the couples more easily agitated.

5. Lifestyle Habits and Health

Independent Variable: Lifestyle habits

Dependent Variable: Health

Extraneous Variable: Income levels, genes, and personality characteristics

It seems that every year the results of a major study on health is released. In a typical study, medical scientists tracked a large group of people for 30 years and examined how their lifestyle impacted their physical health.

The researchers measured various factors such as how much sleep people got on average, their exercise habits, and their diet. At the end of the study all of those variables are compared with measures of physical health such as cholesterol levels and blood pressure. 

Of course, there are all kinds of extraneous variables involved in this type of study. For example, additional factors that may affect health include:

  • Social support
  • Income levels
  • Personality characteristics

If measured, each one of those extraneous variables can be statistically controlled for, which means that their effects can be eliminated statistically to reveal the effects of the predictor variables alone.

6. Field Testing a New App  

Independent Variable: New app

Dependent Variable: Purchasing take-out food

Extraneous Variable: Age of participants

A company has just developed a fantastic new app for hungry users. The app allows users to click on the photo of the meal they want, and then it locates the nearest restaurant that offers that meal for delivery.

To conduct a field test, the company randomly selects people in various mid-sized cities in various regions of the country. The company wants to attract a wide user base, so they include people of all ages that are current users of similar apps.

After three months of field testing, the company notices something unusual about the data. Older users stopped using the app after just one purchase attempt.

During follow-up interviews, the company discovered that the text on their app was too small for older users. They preferred other apps with larger text that are much easier to use.

In this example, age of user is an extraneous variable.

7. Shark Attacks and Ice Cream (Correlation vs. Causation)

Independent Variable: Shark Attacks

Dependent Variable: Ice Cream

Extraneous Variable: Summer

Two variables can be correlated with one another, but that does not mean that one causes the other. The reason is because another variable, a third variable, is playing a causal role, but it’s just not being measured.

A somewhat silly example will make this clear: If a person were to collect data in coastal cities on ice-cream sales and shark attacks, the data would show a clear link. As ice-cream sales goes up, so do shark attacks.

Obviously, there is a third variable at play that is correlated with the two variables being examined; that variable is temperature.

As temperature increases, so do ice-cream sales, and at the same time, so do shark attacks because there are more people swimming in the ocean as the temperature increases.

Third variables are examples of EVs that are always present in correlational research.

8. Fertilizer and Plant Growth

Independent Variable: New fertilizer

Dependent Variable: Crop growth

Extraneous Variable: Individual farming practices

An agricultural company’s team of scientists have developed a unique fertilizer made from organic waste material.

Truckloads of garbage from a local dump are combined with various kinds of insects and worms that eat the garbage and expel a substance that is similar in composition to rich soil.

The research team is ready for testing the fertilizer in the field. They contact farmers in the state and begin to design a study that eliminates as many extraneous variables (EVs) as possible.

They generate a list of EVs that include:

  • duration of daily sunlight
  • existing soil composition
  • estimated precipitation
  • individual farming practices
  • air quality
  • type of crops
  • etc.

Ultimately, the team decides that there are far too many extraneous variables (EVs) to consider. So, they move the testing to their indoor lab. There, they can control all the EVs and make sure each one is the same for each plant.

9. Experimenter Bias

The experimenter is the person that interacts with the research participants in an experiment.

They greet the participants, explain the procedures of the study, distribute various forms and guide the participants through the experimental procedures.

Their role is essential and they receive extensive training in how to be professional and engage in standard actions each and every time the conduct the study.

Unfortunately, people sometimes make mistakes and also have their own biases and inclinations of which they may not be fully aware.  

If the experimenter acts in a biased way towards a particular demographic or gender of participants, it will make the results invalid. Fortunately, experimenter bias is the type of extraneous variable that can be easily eliminated with proper training and precautions.

10. Demand Characteristics

Demand characteristics are any cues in the experiment that might tip-off the participants about the purpose of the study.

These cues could come from anywhere. There could be physical characteristics of the lab that suggest the purpose of the study. Or, the types of questions on a survey of attitudes or personality inventory can suggest a purpose.

These are EVs that can definitely affect the DV and make the results of the study completely invalid. For this reason, it is common practice for researchers to spend considerable time designing the study and carefully considering all possible demand characteristics to eliminate their effects.  


Extraneous variables (EVs) are factors that are related to the phenomenon under study, and they can make the results of research invalid, causing statistical bias.  

For example, if the participants in the study primarily consist of one gender, or one age group, then the results of a study will be affected by the extraneous variable of gender or age. It the experimenter for the study acts in a certain way towards some participants, but not others, then this is also an EV that can invalidate a study’s conclusions.

Other EVs can include the time of day of the study or aspects of the situation where the study took place. Health research is replete with EVs because there are so many factors that affect our health.

Fortunately, with careful planning and design, most EVs can be eliminated so that researchers can isolate the effects of the variables of interest on the phenomenon under study.


Aronson, E., Wilson, T.D., & Brewer, m. (1998). Experimental methods. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), The Handbook of Social Psychology. (4th ed., Vol. 1, pp. 99–142.) New York: Random House.

Campbell, D. T., & Stanley, J. C. (1966). Experimental and Quasi-experimental Designs for Research. Chicago, IL: Rand McNally & Company

Cook, T. D., and Campbell, D. T. (1979). Quasi-Experimentation: Design and Analysis Issues for Field Settings. Houghton Mifflin, Boston.

Kenny, D. A. (2019). Enhancing validity in psychological research. The American Psychologist, 74(9), 1018–1028. https://doi.org/10.1037/amp0000531

Krauth, J. (2000). Control of extraneous variables. Techniques in the Behavioral and Neural Sciences, 14, 37-88. https://doi.org/10.1016/S0921-0709(00)80005-X

Spector, P. (2021). Mastering the use of control variables: The Hierarchical Iterative Control (HIC) approach. Journal of Business and Psychology, 36(1).

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Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

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