Mistaking Correlation for causality is a mistake that goes by many names: "The Fundamental Error of Attribution," "Endogeneity," "Selection bias," or "Measurement Error." Here is a site that computes correlations:
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Here is another one that shows you how to avoid mis-interpreting them:
Given the problems with interpreting correlational data, one might reasonably ask: why do we bother with them at all if it is a causal relationship that we seek? Why not just gather data that could provide a more definite answer, or otherwise just ignore correlations? The reason is pragmatism. Correlational data are usually relatively easy and inexpensive to obtain, at least in comparison to experimental data. Also, many cause-effect relationships are so subtle that we often first learn of them through correlations detected in observational data. That is, they are often useful.
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