Daniel Engber asks why the internet's
favourite blowhard phrase (his words) has gained such popularity:
To say that correlation does not imply causation makes an important point about the limits of statistics, but there are other limits, too, and ones that scientists ignore with far more frequency. In The Cult of Statistical Significance, the economists Deirdre McCloskey and Stephen Ziliak cite one of these and make an impassioned, book-length argument against the arbitrary cutoff that decides which experimental findings count and which ones don't. By convention, we call an effect "significant" if the chances of its deriving from a twist of fate—as opposed to some more genuine relationship—are less than 5 percent. But as McCloskey and Ziliak (and many others) point out, there's nothing special about that number and no reason to invest it with our faith.
It's easy to imagine how this point might be infused into the wisdom of the Web: "Facepalm. How many times do I have to remind you? Don't confuse statistical and substantive significance!" That comment-ready slogan would be just as much a conversation-stopper as correlation does not imply causation, yet people rarely say it. The spurious correlation stands apart from all the other foibles of statistics. It's the only one that's gone mainstream.
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