Spurious Correlation: An Analysis of Variables and Examples

This assignment delves into the concept of spurious correlation, defining it as a relationship between two variables that arises not from a direct connection between them, but from their relationship to other variables. It differentiates between spurious correlation and extraneous variables, which can affect experimental outcomes. The assignment uses an example of students partying and getting sick to illustrate independent, dependent, extraneous variables, and the spurious correlation. It also discusses the temporal relationship between variables, the importance of predictor and outcome variables, and various extraneous factors that can influence the outcome, such as alcohol consumption, health conditions, and drug use. The assignment concludes by examining how these factors interact to produce different outcomes, emphasizing the complexity of identifying causal relationships.

Running Head: SPURIOUS CORELLATION 1
SPURIOUS CORELLATION
Students Name
University of Affiliation

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Spurious correlation is the relationship between two variables which does not result from any
relationship between the two variables however from its relation to additional variables. On the
other hand, extraneous variables are variables that affect the outcome of an experiment other
than the independent and dependent variable(Nugent, 2017). Extraneous variables are considered
to bring about an error in the experiment(T Wilson & Shuttleworth, 2017).
Example
One student had gone out partying the weekend before, and while sitting in the bar watching his
friends during the evening, he noticed that people who had the most fun dancing were also those
who were most likely to throw up by the end of the evening("Spurious Correlation Explained
With Examples", 2017).
In this example, there are the independent variable, dependent variable, extraneous variable and
spurious correlation. In this case being sick is not out of dancing neither does being sick made
the students have fun. Instead, there is an extraneous variable of consumption of alcohol.
Alcohol consumptions lead to dancing and throwing up. If having fun dancing made the students
sick, there must be a positive correlation between having fun dancing and being sick. This is to
mean that it cannot be expected that every time having fun dancing at a party(independent
variable), being sick will also occur(dependent variable) or be sick will only happen after student
having fun dancing at a party.
The temporal relation is another factor that could allow inference to be drawn from the
associated variable relationship (stangor, 2015). Suppose having fun occurs before being sick,
then a conclusion could e brought that being sick is caused by having fun. However, if being sick
only happens before having fun dancing at a party, then having fun could not be creating being

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sick of the student. Then being sick cannot be a casual variable if it only happens after dancing at
the party.
The predictable variable is a variable that is often manipulated to test the outcome of the
dependent variable. On the other hand, the dependent variable is variable that is put under test for
experimental purposes. As the researcher changes the predictor variable, the influence is
observed on the outcome variable and recorded down. In the above example, the experimenter
wants to see attending a party and having student’s dance has any effect on the student being
sick. The researcher observes those students who had most fun dancing. This would be the
predictor variable. How the students felt after that is the outcome variable.
The relationship between the predictor variable and the outcome variable has some extraneous
variables.it is not logic to conclude that every time the student attends a party, have most fun
dancing will end up being sick. There is an extraneous variable that causes the outcome of being
sick. Some of extraneous variable include: first, suppose students attend the party and expose
themselves to cold all night, this could cause sickness such as flue. Secondly, assume the student
has health issues before attending the party. Thirdly, suppose the researcher only selected student
with health conditions to participate in the party to manipulate the result. Lastly, assume the
student consumed a lot of alcohol while at the party that brought about the feeling of the
hangover the following day.
Connection made among spurious variable, outcome variable and predictor variable is as follow:
First, considering the student attended the party, consumed no alcohol, but had fun dancing all
night, this could cause a different outcome. Secondly, if the students participated in the party,
consumed alcohol of varying variety, without even dancing, could cause a student to be sick the

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following day. Thirdly, if the student had health conditions, such as ulcer before attending the
party, consumed alcohol that contained methanol, the student is likely to fall sick without even
dancing at the party. Lastly, the student who attended the party might have fallen sick due to
consuming a lot of alcohol and abusing other drugs and dancing at the party causing fatigue.

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References
URL:https://psychologenie.com/spurious-correlation-explained-with-examples
Nugent, P. (2017). What is EXTRANEOUS VARIABLE? definition of EXTRANEOUS VARIABLE (Psychology
Dictionary).PsychologyDictionary.Retrieved6November2017,from
https://psychologydictionary.org/extraneous-variable/
Spurious Correlation Explained With Examples. (2017). PsycholoGenie. Retrieved 6 November
2017, from https://psychologenie.com/spurious-correlation-explained-with-examples.
T Wilson, L., & Shuttleworth, M. (2017). Confounding Variable / Third Variable.. retrieved Nov 01, 2017
from Explorable.com:. Retrieved 6 November 2017, from https://explorable.com/confounding-
variablesstangor, c. (2015). Research methods for the behavioral sciences (5th ed.).
Stamford,CT: Cengage Learning.