Comment on “Detecting Novel Associations in Large Data Sets”

Reshef et al. presented a novel measure of dependence the maximal information coefficient (MIC) aimed to capture a wide range of associations between pairs of variables, and a statistical test for independence based on MIC. They defined a concept of equitability and claim that non-equitable methods are less practical for data exploration. By simple power comparisons, we show that this conclusion is wrong. ————————– As pointed out by Reshef et al. (Research Article, 17 Dec 2011, p. 1518), it is often the case that the pairwise relationship between many variables is simultaneously explored. In statistics, this exploration is formalized in a multiple hypothesis testing framework, where the null hypothesis of statistical independence is examined for every pair of variables. Then, the p-values of the tests serve as a basis for generating final conclusions. Specifically, the pairs of variables are ordered by their p-values (or the adjusted p-values after correcting for multiple testing) in increasing order, and the pairs with the lowest p-values will be further studied. Reshef et al. To whom correspondence should be addressed