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Adaptive Procedures for the Wilcoxon–Mann–Whitney Test: Seven Decades of Advances

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  • Li Hao
  • Daniel Houser

Abstract

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 an optimal non parametric test. This paper discusses key adaptive approaches for testing differences in locations in two-sample environments. Our Monte Carlo analysis shows that adaptive procedures often perform substantially better than t-tests, even with moderately sized samples (80 observations). We illustrate 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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  • Li Hao & Daniel Houser, 2015. "Adaptive Procedures for the Wilcoxon–Mann–Whitney Test: Seven Decades of Advances," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(9), pages 1939-1957, May.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:9:p:1939-1957
    DOI: 10.1080/03610926.2012.762394
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    Cited by:

    1. Haikady N Nagaraja & Shane Sanders, 2020. "The aggregation paradox for statistical rankings and nonparametric tests," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.

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