Correlation involves a statistical procedure that tests the relationship between quantitative and categorical variables. It describes the level of relatedness between the two variables. The correlation can be positive or a negative, strong or weak (Yadav, 2018). While correlation does explain that there is a relationship or pattern between the two variables, it does not show the nature of that relationship. If the two variables are related, they are correlated. While there may be a level of correlation, a cause-and effect relationship may exist but does not have to exist. Pearson’s correlation coefficient is a statistical analysis tool that helps to quantify that relatedness between two variables (Corty, 2016).
When there is causation, it shows that there is a cause-and-effect relationship between the two variables, that one event caused another to occur. The relationship can also be ambiguous in the direction of cause. This is considered the “chicken and egg” problem, trying to figure which came first. For example, it is found that those with irritable bowel syndrome (IBS) have different gut bacteria compared to healthy or those without IBS. Is it the IBS that causes the different gut bacteria or is it the different gut bacteria that caused the IBS (Chen, 2021)?
There are times when human nature assumes causation due to correlation. In a study by Bleske-Rechek, Morrison, & Heidtke (2015) they sought to examine the degree to which people in the general community draw causal inferences from hypothetical descriptions of experimental and non-experimental research on human behavior. What they found was that people drew causal inferences from non-causal data while drawing inferences that fit with their intuitive notions, regardless of the findings presented (Bleske-Rechek, Morrison, & Heidtke, 2015). This is why caution must be taken when presenting information to the public. It is the non-scientific mind that will draw conclusions about correlation and causation based on their own personal experiences.
References:
Bleske-Rechek, A., Morrison, K. M., & Heidtke, L. D. (2015). Causal inference from descriptions of experimental and non-experimental research: Public understanding of correlation-versus-causation. Journal of General Psychology, 142(1), 48–70. https://doi-org.lopes.idm.oclc.org/10.1080/00221309.2014.977216
Order this paper