Question

Debate the following statement: "Correlation means Causation." Determine whether this statement is true or false, and provide reasoning for your determination, using a Possible Relationships Between Variables table if one is available.

Answer 1

This is a false statement as there is no way that correlation can ever mean causation. Young scholars have often had confusion on the same. When someone says that one variable and another are correlated, they do not mean that one causes the other.

It is also true however to say that the cause and effect relationship between the two variables can be due to another third factorcommonly known as the confounding factor.To prove however if there is a cause and effect relationship, often an experiment is conducted to prove the same. When there is a cause and effect relationship, the relationship between the variables can be proved by collecting data and running them through a multiple regression. If thisis done for correlated factors, the results would show a no relationship by giving a figure of significance of 0.5 or more. When the figure is 0.5 or more, it means that the independent variables chosen do not have any causal effect relationship with the dependent variable.Variables can either be negatively, positively and zero correlated. When we say two variables are positively correlated, all that is being said that is that they tend to move in one direction such thatif one increases, the other increases in the same direction and the vice versa is also true.

The statement “correlation means causation” is not true. The right thing to say is, “correlation is not causation”. In fact, this is a statement that is repeatedly uttered and ingrained in the minds of statisticians.

Accordingly, things could be correlated, but that does not guarantee that one causes the other. In a correlation case, there could be a factor other than the two variables which affects the variables. That would bring about the impression that the two variables have a cause and effect relationship. The factor, which comes in between the variables and affects them both, is usually known as a confounder (Chambers et al, 2015). Consider the decreased shopping that occurs during election years. Although people shop less during this time, it does not mean that elections cause it. Shortage of currency supply due to previous poor monetary policies in the economy could coincide with the election period. Other confounders, too, could be responsible for the correlation between the variables.

Consider the relationship between rainy season and malaria. During rainy seasons in the region, malaria is rampant among the dwellers. The truth is that rain does not cause malaria, but rather the mosquitoes have optimal breeding opportunity and habitat when there is plenty of rain. The confounder here is the mosquito.