09 Jan 2013
Let’s get one thing out of the way first: There is not a single scientist or science journalist who doesn’t know that correlation does not equal causation. Most have probably known it since high school.
That’s why there has been a bit of a backlash against internet commenters who keep pointing it out. The phrase is “common and irritating”, writes Slate’s Daniel Engber in his article The Internet Blowhard’s Favorite Phrase
. To Engber, correlation≠causation is a “freshman platitude” that professional scientists and journalists don’t need to be reminded of. Scientists are merely suggesting
a causal relationship. They are not claiming it is proven.
I can see why writers and researchers find it frustrating to be scolded about something they already know. But correlation≠causation is one of those things that has a way of sliding onto the back burner of one’s mental awareness, even among scientists. On one day a research group is acknowledging the limits to their correlational study, but the next day they are advancing policy arguments that depend on a causal relationship. Or more commonly, they are preparing to run yet another correlational study.
Nowhere does this seem more of an issue than in nutrition science and in education research. In nutrition science, it is much easier to conduct a simple survey on health and eating habits than to organize a large-scale longitudinal randomized control study. Is it any wonder then, that after 50 years of nutrition science we still don’t know
whether saturated fat is good for you or bad for you? And in education research, it is much easier to run a correlational study on class size and achievement than it is to run a randomized control study
. Is it any wonder then, that the public policy debate about class size is so muddled?
Don’t get me wrong – There is some fantastic causal research coming out of the nutrition and education fields. And there is nothing wrong with running a correlational study either. Correlational studies are a great way for researchers to identify variables that are promising enough to investigate with causal methods.
But could it be that we have struck the wrong balance between correlational studies and causal studies? Could we be allocating too many resources towards easy but inconclusive studies, and not enough towards costly but more definitive research? I think the answer might be yes, and that internet comment blowhards are an important voice for this point of view.