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Visualisation: correlation & causation

(Or visualization, if you must)

A great teaching aid for explaining the limits of inference, from the Sources And Methods blog.

Thanks to Erik Hanson, who shared this via Google Reader & Buzz - https://profiles.google.com/erikalanhanson#erikalanhanson/about

It proves conclusively that passport ownership is a cheap and easy cure for diabetes, right?  I mean, look at the maps!  There is almost a perfect correlation between the so-called "diabetes belt" in the south and the lack of ownership of passports in the same region.  Increase the number of passport holders and the diabetes epidemic is over! 

If you have ever heard the classic scientific warning that "correlation does not imply causation" and did not understand what that saying meant, this is a perfect example.  Just because two things are happening at the same time does not necessarily mean that one caused the other.

Analysts typically spring this trap when the connection is not as obviously flawed as it is in this case.  The human mind is extremely good at seeing patterns -- even when they are not there.

Does correlation never indicate causation?  No, that is clearly false as well.   In fact, correlation is a necessary condition for causation -- necessary but not sufficient

The best way to expose this trap appears to be to imagine the counterfactual.  In the case above, imagine what it would be like if all those southerners actually had passports.  Would that, in turn, reduce any of the known risk factors for diabetes?  Unlikely.  It would appear to be merely a coincidence.

See more at sourcesandmethods.blogspot.com