Statnote 23: Non-parametric analysis of variance (ANOVA)

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To carry out an analysis of variance, several assumptions are made about the nature of the experimental data which have to be at least approximately true for the tests to be valid. One of the most important of these assumptions is that a measured quantity must be a parametric variable, i.e., a member of a normally distributed population. If the data are not normally distributed, then one method of approach is to transform the data to a different scale so that the new variable is more likely to be normally distributed. An alternative method, however, is to use a non-parametric analysis of variance. There are a limited number of such tests available but two useful tests are described in this Statnote, viz., the Kruskal-Wallis test and Friedmann’s analysis of variance.

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Original languageEnglish
Number of pages2
Publication statusPublished - Dec 2010


  • analysis of variance, data, non-parametric analysis of variance, Kruskal-Wallis test, Friedmann’s analysis of variance

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