So, Blog, it’s been a while and I have to say, I did not miss you. I would much rather be lazying around watching Zorro (sexy Spanish dude) preferably with a hot water bottle. Enough about me. Let’s talk about correlation and causation. See, if it was Zorro talking about this I think my interest would certainly be engaged.
There are many definitions of correlation available, this one will suffice : “a statistical measurement of the relationship between two variables”. Correlation values range form -1 to +1 (the more negative the value, the more negative the correlation). Causation, however, is the relationship between cause and effect. Now that the definitions are out of the way, can correlation show causality?
The media likes to portray correlation studies as causation, which is wrong as people will often listen to these “proven causes” without even questioning them or looking into the research behind it. An example is a conversation I had with a friend about the implications of medicinal marijuana. She thought it was ridiculous as it has been “proved that cannabis cause schizophrenia”. I asked her… “where did you get this evidence from?” She replied “newspaper”. There is no evidence of causation, only correlation, between cannabis use and schizophrenia. This is because, there could be many other possible variables that can confound the experiment. We can never say for fact that it is one variable alone that causes any change in another. Although many statistical analysis exist, such as multiple regression, which can increase confidence when analysing the relationship, correlation still cannot prove causation.
To show causation one variable must directly cause the change in the other variable. For example, to assert causation participants must be assigned to two groups, one to measure the changed variable, and a control group. The study must have no confounding variables and high validity to suggest causation. This does not mean that correlation studies aren’t important however, as they can build the foundations of further and important studies. It also does not mean that if evidence for causation is strong it still does not mean that a change in A will definitely cause a change in B. As results from a study cannot be 100% generalizable to everybody.