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A New Maximum Test via the Dependent Samples t-Test and the Wilcoxon Signed-Ranks Test
Department of Psychology, University of Windsor, Windsor, Canada;Department of Evaluation and Research, Wayne State University, Detroit, USA
Department of Evaluation and Research, Wayne State University, Detroit, USA
- 1 Department of Psychology, University of Windsor, Windsor, Canada;Department of Evaluation and Research, Wayne State University, Detroit, USA
- 2 Department of Evaluation and Research, Wayne State University, Detroit, USA
Applied Mathematics·Volume 05 (2013)·Pages 110–114·Published 25 December 2013·DOI10.4236/am.2014.51013
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Abstract
A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α while guaranteeing the maximum power of the two constituent tests. Critical values, obtained via Monte Carlo methods, are uniformly smaller than the Bonferroni-Dunn adjustment, giving it power superiority when testing for treatment alternatives of shift in location parameter when data are sampled from non-normal distributions.
KeywordsMaximum TestDependent Samples t-TestWilcoxon Signed-Ranks TestBonferroni-Dunn AdjustmentExperiment-Wise Type I ErrorInferential StatisticsMonte Carlo Method
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