To compare the statistical significance of multiple quantitative variable, the ANOVA test is the way to go. Here, I discuss what you should consider when performing an ANOVA in R.
The benefit of non-parametric tests over parametric tests is that they not make any assumptions about the data. Thus, they are well-suited in situations where the assumptions of parametric tests are not met, which is typically the case for small sample sizes.
Popular non-parametric test
This table gives an overview over popular non-parametric tests:
|Test||Test for what?|
|Wilcoxon rank sum test||Difference in medians|
|Wilcoxon signed-rank test||Difference in paired means|
|Fisher’s exact test||Independence in contingency tables|
|Kruskal-Wallis test||Difference of multiple medians|
Posts about Non-Parametric Significance Testing
Testing whether two groups are independent of each other is a common use case for the Chi-squared and Fisher's exact test. But, under which conditions are these tests appropriate?
If you want to compare the means or medians of paired measurements, you can use a paired Student's t-test or a Wilcoxon signed rank test, respectively. This post explores the properties of these two tests and contrasts them.