Summary

This chapter covered topics of parametric tests. Starting with the assumptions of parametric tests, we identified and applied methods for testing the violation of these assumptions and discussed scenarios where robustness can be assumed when the required assumptions are not met. We then looked at one of the most popular alternatives to the z-test, the t-test. We iterated through multiple applications of this test, covering one-sample and two-sample versions of this test using pooling, pairing, and Welch’s non-pooled version of the two-sample analysis. Next, we explored ANOVA techniques, where we looked at using data from multiple groups to identify statistically significant differences between them. This included one of the most popular adjustments to the p-value for when a high volume of groups is present—the Bonferroni correction, which helps prevent inflating the Type I error when performing multiple tests. We then looked at performing correlation analysis...