The methods for hypothesis testing that we have thus far learned make assumptions about population distributions and their properties. The methods based on these assumptions are called parametric tests. Z-test, t-test, and one- and two-way anova tests. Basically all the techniques we learned so far are called parametric tests. This is one example of a parametric test (more specifically, a t-test) In a normal distribution, mean and variance are called parameters. Statistical tests that assume a distribution and use parameters are called parametric tests. Statistical tests that do not assume a distribution or use parameters are called nonparametric tests. This is the main topic for this week. Nonparametric tests make few assumptions or restrictions on the data. They can be used when the assumptions underlying parametric tests are questionable. When we have non-normal data, we can apply non-parametric statistics without the assumption of normality. When we talk about normal data, this means our data should be at least interval- level.