A parametric test relies upon the assumption that the data you want to test is (or approximately is) normally distributed. Your data needs to be symmetrical, since normally distributed data is symmetrical. If your data does not have the appropriate properties then you use a non-parametric test.
The worked examples in this section do not necessarily have the best experimental designs. They are also purely hypothetical and any results or data are not from any real studies, cases nor experiments. The purpose of them is to demonstrate how to use the various hypothesis tests covered in this section.
There are three main parametric tests
Often it can be difficult to decide whether to use a $z$-test or a t-test as they are both very similar. Here are some tips to help you decide:
The following diagram can be used to help you decide which test is appropriate too.
Try our Numbas tests on parametric hypothesis tests and two-sample tests.
Click here for some more worked examples (from the Business page).
See also non-parametric hypothesis testing and tests on frequencies for information on other types of hypothesis testing.