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Linking correlation to causation with power laws and scale free systems

An essential part of science involves finding correlations between two sets of measurements and seeking explanations for those correlations. However, relationships can be suggested by data even when they don't actually exist, and correlations may occur due to random fluctuations rather than a deep underlying principle (as the infamous "correlation does not equal causation" cliché suggests). These errors are easy to make, and the scientific literature is full of them.

So how can researchers establish if a correlation is both real and meaningful? In a Perspective in the February 10 issue of Science, Michael P.H. Stumpf and Mason A. Porter examine the type of correlation known as a power law, where one set of measurements is related to a second via an exponent. They argue that two things must be in place for a power law to be valid as a predictive model: it must hold over a wide range of data to eliminate chance associations, and it must have a plausible mechanism to explain why the correlation showed up in the data.

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from Ars Technica http://arstechnica.com/science/news/2012/02/seeing-a-power-law-in-data-doesnt...