What is the non-parametric equivalent of a one-way ANOVA used for comparing multiple groups?

The non-parametric equivalent of a one-way ANOVA, which is used for comparing multiple independent groups, is the Kruskal-Wallis Test. This test is particularly useful in situations where the assumptions of normality and homogeneity of variances required for a one-way ANOVA cannot be met.

The Kruskal-Wallis Test operates by ranking all the data points regardless of the group they belong to and then assessing whether there are statistically significant differences in the ranks between the groups. This approach makes it suitable for ordinal data or for data that do not follow a normal distribution.

Understanding the context of other methods helps clarify why they do not serve as the non-parametric alternative in this case. The Friedman Test, for example, is also a non-parametric test, but it is specifically designed for repeated measures rather than for independent groups, making it a different type of analysis altogether. Stepwise Regression is a statistical method for selecting a subset of independent variables for linear regression and does not pertain to comparing group means. Lastly, ANOVA itself is a parametric test and not non-parametric, so it does not qualify as an alternative.

In summary, the Kruskal-Wallis Test is the appropriate non-parametric method for comparing more