The following is an exerpt from a blog post by Kristan J. Wheaton, Associate Professor of intelligence studies at Mercyhurst College, is a summarized example of the analysis of "correlation and causation".
ISSUED BY: GCIS Communications Command Center
SOURCE: Sources And Methods
21March2011 1:23pmEST
GCIS INTELLIGENCE UPDATE: 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.
Should the analyst just discard the evidence at this point? The answer is "No" once again. (read full report)
