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2010 Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules
Michael P. Fay, Michael A. Proschan
Statist. Surv. 4: 1-39 (2010). DOI: 10.1214/09-SS051


In a mathematical approach to hypothesis tests, we start with a clearly defined set of hypotheses and choose the test with the best properties for those hypotheses. In practice, we often start with less precise hypotheses. For example, often a researcher wants to know which of two groups generally has the larger responses, and either a t-test or a Wilcoxon-Mann-Whitney (WMW) test could be acceptable. Although both t-tests and WMW tests are usually associated with quite different hypotheses, the decision rule and p-value from either test could be associated with many different sets of assumptions, which we call perspectives. It is useful to have many of the different perspectives to which a decision rule may be applied collected in one place, since each perspective allows a different interpretation of the associated p-value. Here we collect many such perspectives for the two-sample t-test, the WMW test and other related tests. We discuss validity and consistency under each perspective and discuss recommendations between the tests in light of these many different perspectives. Finally, we briefly discuss a decision rule for testing genetic neutrality where knowledge of the many perspectives is vital to the proper interpretation of the decision rule.


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Michael P. Fay. Michael A. Proschan. "Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules." Statist. Surv. 4 1 - 39, 2010.


Published: 2010
First available in Project Euclid: 22 February 2010

zbMATH: 1188.62154
MathSciNet: MR2595125
Digital Object Identifier: 10.1214/09-SS051

Keywords: Behrens-Fisher problem , interval censored data , nonparametric Behrens-Fisher problem , Tajima’s D , t-test , Wilcoxon rank sum test

Rights: Copyright © 2010 The author, under a Creative Commons Attribution License


Vol.4 • 2010
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