Abstract
The nonparametric Wilcoxon–Mann–Whitney test is commonly used by experimental economists for detecting differences in central tendency between two samples. This test is only theoretically appropriate under certain assumptions concerning the population distributions from which the samples are drawn, and is often used in cases where it is unclear whether these assumptions hold, and even when they clearly do not hold. Fligner and Pollicello's (1981, Journal of the American Statistical Association. 76, 162–168) robust rank-order test is a modification of the Wilcoxon–Mann–Whitney test, designed to be appropriate in more situations than Wilcoxon–Mann–Whitney. This paper uses simulations to compare the performance of the two tests under a variety of distributional assumptions. The results are mixed. The robust rank-order test tends to yield too many false positive results for medium-sized samples, but this liberalness is relatively invariant across distributional assumptions, and seems to be due to a deficiency of the normal approximation to its test statistic's distribution, rather than the test itself. The performance of the Wilcoxon–Mann–Whitney test varies hugely, depending on the distributional assumptions; in some cases, it is conservative, in others, extremely liberal. The tests have roughly similar power. Overall, the robust rank-order test performs better than Wilcoxon–Mann–Whitney, though when critical values for the robust rank-order test are not available, so that the normal approximation must be used, their relative performance depends on the underlying distributions, the sample sizes, and the level of significance used.
Similar content being viewed by others
References
Boos, D.D. and Brownie, C. (1988). “Bootstrap p-Values for Tests of Nonparametric Hypotheses.” Institute of Statistics Mimeo Series No. 1919, North Carolina State University.
Cohen, J. (1969). Statistical Power Analysis for the Behavioral Sciences. New York and London: Academic Press.
Duffy, J. and Feltovich, N. (1999). “Does Observation of Others Affect Learning in Strategic Environments? An Experimental Study.” International Journal of Game Theory. 28, 131–152.
Feltovich, N. (2003). “Critical Values for the Robust Rank-Order Test.” Working paper, University of Houston. Available at www.uh.edu/ ~nfelt/papers/rrovals.pdf
Fligner, M.A. and Pollicello, G.E. III. (1981). “Robust Rank Procedures for the Behrens-Fisher Problem.” Journal of the American Statistical Association. 76, 162–168.
Mann, H.B. and Whitney, D.R. (1947). “On a Test of Whether One of Two Random Variables is Stochastically Larger Than the Other.” Journal of Statistical Computing and Simulation. 13, 41–48.
Siegel, S. and Castellan, N.J., Jr. (1988). Nonparametric Statistics for the Behavioral Sciences. New York: McGraw-Hill.
Tatsuoka, M. (1993). “Effect Size.” In G. Keren and C. Lewis (eds.), A Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues. Hillsdale, NJ: Erlbaum, pp. 461–479.
Wilcoxon, F. (1945). “Individual Comparisons by Ranking Methods.” Biometrics. 3, 119–122.
Zimmerman, D.W. (1987). “Comparative Power of Student T Test and Mann-Whitney U Test for Unequal Sample Sizes and Variances.” Journal of Experimental Education. 55, 171–174.
Zimmerman, D.W. and Zumbo, B.D. (1993a). “Rank Transformations and the Power of the Student t Test and Welch t test for non-normal populations with unequal variances.” Canadian Journal of Experimental Psychology. 47, 523–539.
Zimmerman, D.W. and Zumbo, B.D. (1993b). “The Relative Power of Parametric and Nonparametric Statisti-cal Methods.” In G. Keren and C. Lewis (eds.), A Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues. Hillsdale, NJ: Erlbaum, pp. 481–517.
Zumbo, B.D. and Coulombe, D. (1997). “Investigation of the Robust Rank-Order Test for Non-Normal Populations with Unequal Variances: The Case of Reaction Time.” Canadian Journal of Experimental Psychology. 51, 139–149.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Feltovich, N. Nonparametric Tests of Differences in Medians: Comparison of the Wilcoxon–Mann–Whitney and Robust Rank-Order Tests. Experimental Economics 6, 273–297 (2003). https://doi.org/10.1023/A:1026273319211
Issue Date:
DOI: https://doi.org/10.1023/A:1026273319211