In this chapter, we've learned about the difference between descriptive and inferential statistics. Once again, we've seen the importance of normal distribution and the central limit theorem, and learned how to quantify population differences with z-tests, t-tests, and F-tests.

We've learned about how the techniques of inferential statistics analyze the samples themselves to make claims about the population that was sampled. We've seen a variety of techniques—confidence intervals, bootstrapping, and significance tests—that can yield insight into the underlying population parameters. By simulating repeated tests with ClojureScript, we've also gained an insight into the difficulty of significance testing with multiple comparisons and seen how the F-test attempts to address the issue and strike a balance between Type I and Type II errors.

In the next chapter, we'll apply the lessons we've learned on variance and F-testing to single samples. We'll introduce the technique of regression...