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Robust Two-Sample Statistics for Testing Equality of Means: A Simulation Study

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  • James Reed
  • David Stark

Abstract

When testing the equality of the means from two independent normally distributed populations given that the variances of the two populations are unknown but assumed equal, the classical two-sample t-test is recommended. If the underlying population distributions are normal with unequal and unknown variances, either Welch's t-statistic or Satterthwaite's Approximate F-test is suggested. However, Welch's procedure is non-robust under most non-normal distributions. There is a variable tolerance level around the strict assumptions of data independence, homogeneity of variances and normality of the distributions. Few textbooks offer alternatives when one or more of the underlying assumptions are not defensible.

Suggested Citation

  • James Reed & David Stark, 2004. "Robust Two-Sample Statistics for Testing Equality of Means: A Simulation Study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 831-854.
    • Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:831-854
    DOI: 10.1080/0266476042000214529
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    Cited by:

    1. Miao, Weiwen & Chiou, Paul, 2008. "Confidence intervals for the difference between two means," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2238-2248, January.
    2. Roland Fried & Herold Dehling, 2011. "Robust nonparametric tests for the two-sample location problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 409-422, November.

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