Sample Psychology Essays on Correlation vs. Causation

Correlation is the most commonly used statistical measure for determining relationships among two variables. However, the presence of a relationship does not necessarily mean that one variable can cause the other. Causation examines the cause and effect relationship between variables. Often, misleading interpretations of correlation indicate causation. It is important to note that correlation doesn’t always imply causation.

The relationship between demand and price is an example of a meaningful correlation. There is a positive correlation between demand and price. It means that the demand for a product is positively correlated with the associated price. The relationship between demand and supply indicates a causational and positive correlation. When the product demand rises, there is a corresponding increase in the price of that product. The price increases because the more the product is wanted by consumers, the more the price they are willing to pay (Sharma, 2013).

Misleading correlations are also known as spurious correlations. These correlations often indicate a causal connection between two variables which is not there. Sometimes a relationship between two variables may be purely coincidental or can only be explained by a third factor called the confounding factor. Misleading correlation can also be caused by the use of small sample sizes. An example of misleading correlation is the correlation between the number of fighters in afire incidence and the amount of damaged caused by the fire is likely to be positive. One should be careful not to quickly conclude that the presence of a large number of fighter fighters will automatically influence the damaged caused.  The real cause of the relationship between these two variables can be explained by size of the fire which is a confounding factor. The factor affects both of these variables (Groth, 2013).

Correlation vs. Causation Relationship

Causation and correlation are not equivalent terms as illustrated by the misleading correlations. The correlation only indicates a relationship that shows that two variables are moving together. However, correlation measures alone cannot predict whether the change in the two variables is as a result of one variable causing the other (Curran-Everett, 2010). Causation indicates a cause and effect relationship by identifying the presence of a true relationship between the two variables (SAS Institute Inc.). Often, a positive correlation indicates causation but it may not always be the case. The relationship between the variables may be coincidental or as a result of a third variable causing concurrent changes in the two.

It is important to distinguish whether one variable causes the other before making conclusions. The evidence for causation between two variables is established though experimental designs. Increased heart disease may be correlated with the consumption of high-fat diets. The correlations look reliable and indicate causational. However, a further empirical investigation is needed to establish if one of the variables causes the other. Correlation simply shows a connection between the two variables from the observed data such as the reported diets and the rate of heart diseases. Causation will help find evidence of this relationship (SAS Institute Inc.).

The relationship between the two variables can be statistically determined through correlation. However, correlation alone may not indicate a true relationship between the variables under study. Causation is conducted to carefully analyze the observe data and give a meaningful interpretation of the relationship between the two variables.



Curran-Everett, D. (2010). Explorations in statistics: correlation. Advances in Physiology Education34(4), 186–191. Doi: 10.1152/advan.00068.2010

Groth, R. E. (2013). Teaching mathematics in grades 6-12: developing research-based instructional practices. Los Angeles: SAGE.

SAS Institute Inc. (n.d.). Correlation vs Causation. Retrieved from

Sharma, J. K. (2013). Business statistics. New Delhi: Pearson.