In this business assignment, we have shed light on How Spurious correlation works. Spurious correlation is a mathematical relationship in which two or more variables or events seem to be causally related but they are not actually related. They show a relationship either due to wrong inference or coincidence. Spurious correlation may also be observed because of existence of a third, unseen variable. I have seen various spurious correlations on website (http://tylervigen.com/spurious-correlations) and some of them are very interesting and funny. For example, the relationship between “US spending on science, space, and technology” and “suicide by hanging, strangulation, and suffocation” (Vigen, 2015). Following are the variables:
Variable 1: US spending on science, space, and technology
Variable 2: Suicide by hanging, strangulation, and suffocation
It is not possible to determine any relationship between these two variables on logical grounds. The relationship between these two variables is because of coincidence. The statistical data suggests that the US spending on science, space, and technology has increased over the period of time. Similar is the case with suicide by hanging, strangulation, and suffocation. The value of correlation coefficient in this case is 99.79% which means that the relationship between the two variable is very strong (Simon, 1977). However, there is no means by which one can infer an increase in number of suicides by hanging, strangulation, and suffocation due to an increase in spending on science, space, and technology. This relationship must be a spurious relationship which is observed only because of coincidence otherwise it is not possible to draw any logical inference. There is no indication of existence of any third variable. It means that the relationship is observed only because of coincidence. It is a spurious correlation which does not actually exist (Haig, 2003).
Another spurious correlation which I studied on this website was the relationship between the number of people who drowned by falling into a pool and films Nicholas Cage appeared in. It is a very funny correlation which shows that the number of people who drowned by falling into a pool increased as the number of Nicholas Cage’s appearing in the films increased. It is easy to infer that there is no logical relationship between Nicholas Cage’s appearing and number of people who drowned in the swimming pools (Chang, 2003). The value of correlation coefficient is 66.7% which shows that the relationship between the variables is quite strong. Regardless of the fact that the number of people who drown whenever Nicholas Cage appears in the movie, one cannot restrict Nicholas Cage from appearing in the movie because Nicholas Cage has nothing to do with people who want to swim. The relationship between these two variables is spurious. The statisticians observed this relationship only because of coincidence. It is not possible to draw a logical relationship between these two variables (Calude, 2017).
In both of these examples, it is not possible to infer the causal relationship between the variables on logical grounds and there is no indication of any third variable. It means that the relationship between these two variables was observed only because of coincidence and the relationship does not actually exist.
Calude, C. S. (2017). The deluge of spurious correlations in big data. Foundations of science, 22(3), 595-612.
Chang, N. K. (2003). Learning spurious correlations instead of deeper relations. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 25, No. 25).
Haig, B. D. (2003). What is a spurious correlation?. Understanding Statistics: : Statistical Issues in Psychology. Education, and the Social Sciences, 2(2), 125-132.
Simon, H. A. (1977). Models of Discovery: and Other Topics in the Methods of Science. Springer Science & Business Media.
Vigen, T. (2015). Spurious Correlations. Hachette Books.
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