### Comparing Measurements Across Several Groups: ANOVA

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.

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 |

Fisherâ€™s exact test | Independence in contingency tables |

Kruskal-Wallis test | Difference of multiple medians |

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.

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.