12.7. Summary

In this chapter, we introduced a set of computer-intensive methods based on randomization and resampling that allow you to test hypotheses and form confidence intervals without reference to a known theoretical distribution. They’re particularly valuable when your data comes from unknown population distributions, when there are serious outliers, when your sample sizes are small, and when there are no existing parametric methods to answer the hypotheses of interest.

The methods in this chapter are particularly exciting because they provide an avenue for answering questions when your standard data assumptions are clearly untenable, or when you have no other idea how to approach the problem. Permutation tests and bootstrapping aren’t panaceas, though. They can’t turn bad data into good data. If your original samples aren’t representative of the population of interest, or are too small to accurately reflect it, then these techniques won’t help.