Robustness of the t and U tests under combined assumption violations

When the assumptions of parametric statistical tests for the difference between two means are violated, it is commonly advised that non-parametric tests are a more robust substitute. The history of the investigation of this issue is summarized. The robustness of the t -test was evaluated, by repeate...

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Bibliographic Details
Published in:Journal of applied statistics 1998-02, Vol.25 (1), p.63-74
Main Authors: Stonehouse, John M., Forrester, Guy J.
Format: Article
Language:English
Subjects:
Hypotheses
Normal distribution
Population
Sample size
Standard deviation
Statistical analysis
Statistical methods
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Summary:When the assumptions of parametric statistical tests for the difference between two means are violated, it is commonly advised that non-parametric tests are a more robust substitute. The history of the investigation of this issue is summarized. The robustness of the t -test was evaluated, by repeated computer testing for differences between samples from two populations of equal means but non-normal distributions and with different variances and sample sizes. Two common alternatives to t -Welch's approximate t and the Mann-Whitney U -test-were evaluated in the same way. The t -test is sufficiently robust for use in all likely cases, except when skew is severe or when population variances and sample sizes both differ. The Welch test satisfactorily addressed the latter problem, but was itself sensitive to departures from normality. Contrary to its popular reputation, the U -test showed a dramatic 'lack of robustness' in many cases-largely because it is sensitive to population differences other than between means, so it is not properly a 'non-parametric analogue' of the t -test, as it is too often described.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664769823304