Chapter 21
What Is Normal Anyway?
IN THIS CHAPTER
Introducing nonparametric tests
Viewing distributions
Using the Kruskal Wallis test
Using the Wilcoxon signed-rank test
Parametric tests — such as the t-tests, ANOVA, Pearson correlations, and linear regression — make several assumptions about the data. They typically assume that the variables have a normal distribution for continuous variables, and that the variance is equal within categories of a grouping or factor variable (homogeneity of variance). If these assumptions are violated, the results of the tests are in doubt. Starting with Chapter 16, we’ve been discussing how to check statistical assumptions, but we haven't provided a path forward if you fail to meet them.
Fortunately, there are alternatives. An entire family of various tests and methods called nonparametric statistics make fewer assumptions about the data. Nonparametric tests allow you to determine if relationships exist in the data when important distributional assumptions are not met.
In this chapter, we begin by introducing nonparametric tests. Then we discuss ...
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