The tests for comparison of population try to determine if K (K 2) samples come from the same underlying population according to a dependent variable (X). In other words, we try to determine if the underlying distribution of X is the same whatever the group.
We talk about non parametric tests when we do not make assumption about the shape of the distribution of the dependent variable. They are considered as being "distribution free" methods, at the opposite of the parametric approaches.
In this tutorial, we implement various tests for differences in location. The Kruskal-Wallis test is certainly the most used one when we try to determine if the scores among groups are stochastically the same. But other tests exist. We compare the results obtained. We will complete the analysis by conducting multiple comparisons in order to identify groups that differ significantly from each other.
Keywords: non parametric test, independent samples, Kruskal-Wallis, Van der Waerden, Fisher-Yates-Terry-Hoeffding, median test, tests for differences in location
Components: KRUSKAL-WALLIS 1-WAY ANOVA, MEDIAN TEST, VAN DER WAERDEN 1-WAY ANOVA, FYTH 1-WAY ANOVA
Tutorial: en_Tanagra_Nonparametric_Test_KW_and_related.pdf
Dataset: wine_evaluation_nonparametric.xls
References:
R. Lowry, « Concepts and Applications of Inferential Statistics », SubChapter 14a. The Kruskal-Wallis Test for 3 or More Independent Samples.
Wikipedia. Kruskal–Wallis one-way analysis of variance.
