Kruskal-Wallis test

Kruskal-Wallis test: It is a nonparametric  test, and is used when the assumptions of one-way ANOVA are not met.
In the ANOVA, we assume that the dependent variable is normally distributed and there is approximately equal variance on the scores across groups. While we use kruskal Wallis test when these assumptions are not met. Therefore, the Kruskal-Wallis test can be used for both continuous and ordinal-level dependent variables.  However, like most non-parametric tests, the Kruskal-Wallis Test is not as powerful as the ANOVA.

Null hypothesis: samples (groups) are from identical populations.

Alternative hypothesis: at least one of the samples (groups) comes from a different population than the others.

The distribution of the Kruskal-Wallis test statistic approximates a chi-square distribution, with k-1 degrees of freedom, if the number of observations in each group is 5 or more.  If the calculated value of the Kruskal-Wallis test is less than the critical chi-square value, then the null hypothesis cannot be rejected.  If the calculated value of Kruskal-Wallis test is greater than the critical chi-square value, then we can reject the null hypothesis and say that at least one of the samples comes from a different population.

Assumptions:
1. Sample drawn is random
2. Observations are independent of each other
3. Measurement scale for the dependent variable is atleast ordinal

PSM / COMMUNITY MEDICINE by Dr Abhishek Jaiswal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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