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2 results

  • 7 - Non-parametric Methods for Two Samples

  • Jan Lepš, University of South Bohemia, Czech Republic, Petr Šmilauer, University of South Bohemia, Czech Republic
  • Book:
    Biostatistics with R
    Published online:
    18 September 2020
    Print publication:
    30 July 2020, pp 92-103

  • Summary

    After a general introduction to nonparametric tests, we review two useful nonparametric tests for comparing two samples. The Mann-Whitney test is a counterpart of the two-sample t test, but it uses the ranks of recorded values instead. Despite this test often being described as a test of the differences between mean values, this only applies when both samples come from distributions where both distribution curves are of the same shape. The Wilcoxon test for paired observations corresponds to the parametric paired t test. We also introduce permutation tests, which represent another group of non-parametric methods for hypothesis testing. The methods described in this chapter are accompanied by a carefully-explained guide to the R code needed for their use.

  • 10 - Using Non-Parametric Tests

  • from Part II - How to Use Statistics
  • Paul Cairns, University of York
  • Book:
    Doing Better Statistics in Human-Computer Interaction
    Published online:
    26 January 2019
    Print publication:
    07 February 2019, pp 114-124

  • Summary

    Non-parametric tests, in particular rank-based tests, are often proposed as robust alternatives to parametric tests like t-tests when the assumptions of parametric tests are violated. However, non-parametric tests have their own assumptions which, when not considered, can lead to misinterpretation and unsound conclusions based on those tests. This chapter explores these problems and differentiates between the more and less robust non-parametric tests. Modern robust alternative non-parametric tests are suggested to replace the less robust tests.