Hm, this and some of the other comments being mentioned are the much less interesting instances of "correlation != causation". Specifically, these examples (in

Hm, this and some of the other comments being mentioned are the much less interesting instances of "correlation != causation". Specifically, these examples (including the Superbowl indicator) are assumed to be cases of coincidence (with enough data, certain slices of noise will look like signal). By contrast, the much more interesting and relevant examples are things like hidden variables: a third factor that has causal relationships (in whatever direction) with both of the correlated variables being described, even though the two correlated variables have no such causal relationship. For an extremely trivial example, the data likely supports the conclusion that you're more likely to get into car accidents when you have an umbrella with you. This obviously doesn't imply that having an umbrella in your car causes more car accidents: but rather that the hidden variable of rainy weather causes both "people have umbrellas with them" and "people get into more car accidents". Controlling for rainy weather (i.e. examining the correlation during sunny days only or rainy days only), you'd likely find that the correlation vanishes.