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.
Parametric significance tests assume that the data follow a specific distribution (typically the normal distribution). If their assumptions are met, they have greater power than non-parametric test. Otherwise, non-parametric tests should be used. Thus, parametric tests should only be used after carefully evaluating whether the assumptions of the test are sufficiently fulfilled.
This table gives an overview of the most popular parametric tests:
|Test||Test for what?|
|Student’s t-test, Paired Student’s t-test||Difference in paired means and means|
|Chi-squared test||Independence of group counts|
|One-way ANOVA||Difference in means of several independent variables|
Posts about Parametric Significance Testing
Parametric tests require that data are normally distributed. Here, you will learn how many samples are necessary to satisfy the assumptions of parametric tests.
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.