Abstract
It is known that the Wilcoxon-Mann-Whitney test is strongly influenced by unequal variances of treatment groups combined with unequal sample sizes. This simulation study indicates that, for various continuous and discrete distributions, the discrepancy between the empirical Type I error rate and the nominal significance level is large even when sample sizes are equal. In some cases, it exceeds the similar discrepancy characteristic of the Student t test. Furthermore, for some distributions, the discrepancy becomes increasingly more extreme as sample sizes increase. When sample sizes are relatively large, so that the normal-approximation form of the Wilcoxon-Mann-Whitney statistic is appropriate, minor and usually undetected differences in variability of treatment groups can substantially inflate the Type I error rate. For several distributions, including some that occur frequently in psychological research, ratios of population standard deviations as small as 1.1 or 1.2 have sizeable effects.