Tests for mean equality that do not require homogeneity of variances: Do they really work?

H. J. Keselman, Rand R. Wilcox, Jason Taylor, Rhonda K. Kowalchuk

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    Tests for mean equality proposed by Weerahandi (1995) and Chen and Chen (1998), tests that do not require equality of population variances, were examined when data were not only heterogeneous but, as well, nonnormal in unbalanced completely randomized designs. Furthermore, these tests were compared to a test examined by Lix and Keselman (1998), a test that uses a heteroscedastic statistic (i.e., Welch, 1951) with robust estimators (20% trimmed means and Winsorized variances). Our findings confirmed previously published data that the tests are indeed robust to variance heterogeneity when the data are obtained from normal populations. However, the Weerahandi (1995) and Chen and Chen (1998) tests were not found to be robust when data were obtained from nonnormal populations. Indeed, rates of Type I error were typically in excess of 10% and, at times, exceeded 50%. On the other hand, the statistic presented by Lix and Keselman (1998) was generally robust to variance heterogeneity and nonnormality. Copyright © 2000 by Marcel Dekker, Inc.
    Original languageEnglish
    Pages (from-to)875-895
    Number of pages20
    JournalCommunications in Statistics - Theory and Methods
    Issue number3
    Publication statusPublished - 2000


    • Monte Carlo
    • Nonnormality
    • Robust Estimators
    • Tests for Mean Equality
    • Variance Heterogeneity


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