The Wilcoxon-Mann Whitney test has dominated non-parametric analyses in behavioral sciences for the past seven decades. Its widespread use masks the fact that there exist simple "adaptive" procedures which use data-dependent statistical decision rules to select optimal non-parametric test. This paper discusses key adaptive approaches for testing differences in locations in two-sample environments. The Monte-Carlo analysis shows that adaptive procedures often perform substantially better then t-tests, even with moderately sized samples (80 observations). It illustrates adaptive approaches using data from Gneezy and Smorodinsky (2006), and offer a Stata package to researchers interested in taking advantage of these techniques.
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