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The parametric equivalent of the Kruskal–Wallis test is the one-way analysis of variance (ANOVA). A significant Kruskal–Wallis test indicates that at least one sample stochastically dominates one other sample. The test does not identify where this stochastic dominance occurs or for how many pairs of groups stochastic dominance obtains.
There are some alternatives to conventional one-way analysis of variance, e.g.: Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test and Kruskal-Wallis test, available in onewaytests R
William Henry Kruskal (/ ˈ k r ʌ s k əl /; October 10, 1919 – April 21, 2005) was an American mathematician and statistician. He is best known for having formulated the Kruskal–Wallis one-way analysis of variance (together with W. Allen Wallis ), a widely used nonparametric statistical method .
In statistics, the Jonckheere trend test [1] (sometimes called the Jonckheere–Terpstra [2] test) is a test for an ordered alternative hypothesis within an independent samples (between-participants) design. It is similar to the Kruskal-Wallis test in that the null hypothesis is that several independent samples are from the same population ...
Download as PDF; Printable version; ... Kruskal-Wallis test [11] Wilcoxon signed-rank test: ... Normality test: sample size between 3 and 5000 ...
The Kruskal-Wallis test is designed to detect stochastic dominance, so the null hypothesis is the absence of stochastic dominance. Using multi-modal distributions you can quickly generate counter examples to the claim "the null hypothesis of the Kruskal-Wallis is equal distribution of the samples".
The most common non-parametric test for the one-factor model is the Kruskal-Wallis test. The Kruskal-Wallis test is based on the ranks of the data. The advantage of the Van Der Waerden test is that it provides the high efficiency of the standard ANOVA analysis when the normality assumptions are in fact satisfied, but it also provides the ...
Kruskal–Wallis one-way analysis of variance by ranks: tests whether > 2 independent samples are drawn from the same distribution. Kuiper's test: tests whether a sample is drawn from a given distribution, sensitive to cyclic variations such as day of the week. Logrank test: compares survival distributions of two right-skewed, censored samples.