Which is the best nonparametric test for unequal variance?
The Kruskal-Wallis test is a rank-based analogue of 1-way ANOVA, so it would be a reasonable approach to nonparametric testing of differences in location for >=2 groups. HOWEVER: the “unequal variance” thing really messes you up.
What are the reasons to use a nonparametric test?
The main reasons to apply the nonparametric test include the following: 1. The underlying data do not meet the assumptions about the population sample Generally, the application of parametric tests requires various assumptions to be satisfied.
Which is the nonparametric counterpart of the t-test?
Wilcoxon Signed Rank Test The Wilcoxon Signed Rank Test is a nonparametric counterpart of the paired samples t-test. The test compares two dependent samples with ordinal data. 3.
When to use a nonparametric or one way ANOVA?
The Kruskal-Wallis Test is a nonparametric alternative to the one-way ANOVA. The Kruskal-Wallis test is used to compare more than two independent groups with ordinal data.
When are unequal sample sizes are and are not a problem in ANOVA?
So if you have equal variances in your groups and unequal sample sizes, no problem. If you have unequal variances and equal sample sizes, no problem. The only problem is if you have unequal variances and unequal sample sizes.
Which is the rule of thumb for unequal variances?
Dean and Voss ( Design and Analysis of Experiments, 1999, page 112) suggest a rule of thumb to answer this question: if the ratio of the largest treatment variance estimate to the smallest treatment variance estimate does not exceed 3, s m a x 2 / s m i n 2 < 3, the assumption is probably satisfied.
Why are unequal variances problematic for Mann Whitney tests?
HOWEVER: the “unequal variance” thing really messes you up. This answer discusses why unequal variances are problematic for Mann-Whitney tests (the 2-sample version of K-W/non-parametric version of the t-test), and the same problem applies to K-W, as discussed on Wikipedia (linked above):