Correlation and Causation

Correlation and Causation

Correlation and causation are among the statistical measures commonly misinterpreted. This is because both explain the changes in one variable in relation to a change in another variable. For correlation, it tells how one variable behaves in relation to a change in another variable (Green, 2012). This is mostly indicated by interpreting a correlation coefficient value that falls between -1 and +1. Essentially, a value close to -1 shows a strong negative correlation. Contrariwise, a value close to +1 shows a strong positive correlation. On the other hand, causation tells or shows whether one variable directly impacts the outcome of the other variable. Thus, it helps an individual establish whether one variable causes the other. For instance, temperatures and volume of ice melted can be viewed as an instance of causation since temperatures directly contribute to the change in the volume of ice melted.

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An article published by the Guardian shows an example of an instance where correlation is misinterpreted as causation (Green, 2012). Green gives a seasonal example in the UK, where people tend to misinterpret the tendency of increased spending in shops during cold seasons as an instance of causation. Green (2012) corrects the interpretation and asserts that just because people spend more when it is cold and less when it is hot does not mean there is a causation between weather and spending. The high spending could actually be due to the coincidence between cold weather and Christmas and New Year sales. Therefore, the relationship between cold weather and high spending can be seen as a positive correlation rather than a causation. This is because correlation can be due to a third confounding factor that affects both variables of interest.

References

Green, N. (2012). Correlation Is Not Causation. Retrieved from https://www.theguardian.com/science/blog/2012/jan/06/correlation-causation#:~:text=%22Correlation%20is%20not%20causation%22%20means,causes%20frenzied%20high%2Dstreet%20spending.a:link {text-decoration: none;}a:visited {text-decoration: none;
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One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression.

Correlation and Causation

It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow to say that there is any causal link between the two variables. In other words, we cannot say that one variable causes another; however, it is not uncommon to see such use in the news media. An example is shown below.

Here we see that, at least visually, there appears to be a relation between the divorce rate in Maine and the per capital consumption of margarine. Does this imply that all married couples in Maine should immediately stop using margarine in order to stave off divorce? Common sense tells us that is probably not true.

This is an example of a spurious correlation in which there appears to be a relation between the divorce rate and margarine consumption but, it is not a causal link. The appearance of such a relation could merely be due to coincidence or perhaps another unseen factor.

What is one instance where you have seen correlation misinterpreted as causation? Please describe. This serves as your initial post to the discussion