Investigation of Location-Scale Nonparametric Tests and Their Implementation in
SAS For Two-sample Problem

Freeh N. Alenezi

Department of Mathematics, Zulfi College of Science, Majmaah University

Department of Statistics, West Virginia University.


Parametric statistical methods require assumptions, one of which is the normality assumption that is often difficult to meet. In contrast, nonparametric statistical methods do not require a normal distribution in a given population. This report focuses on the implementation of location-scale nonparametric tests in SAS for two-sample problem. The location-scale nonparametric tests, Lepage and Cucconi tests, the location test despite any possible difference in variability between two groups, Permutation Test with Brunner & Munzel statistic, and the location nonparametric test, Wilcoxon Rank Sum test, were applied on different random samples to assist their performance. The Permutation Test with Brunner & Munzel statistic was observed to have the best performance among other tests in Part 1. In Part 2, the P-values for all location-scale nonparametric tests have no significant difference with respect to small and large means and variances for the generated random samples. The P-values of the Wilcoxon Rank Sum test have no significant difference from the P-values of the Permutation Test with Brunner & Munzel statistic where the latter has significantly the smallest P-values among other tests.


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