Section author: Danielle J. Navarro and David R. Foxcroft

Summary

The first half of this chapter was focused primarily on the theoretical underpinnings of Bayesian statistics. I introduced the mathematics for how Bayesian inference works (section Probabilistic reasoning by rational agents), and gave a very basic overview of how Bayesian hypothesis tests are typically done. Finally, I devoted some space to talking about why I think Bayesian methods are worth using (section Why be a Bayesian?). Then I gave a practical example, a Bayesian t-tests.

If you’re interested in learning more about the Bayesian approach, there are many good books you could look into. John Kruschke’s (2015) book Doing Bayesian Data Analysis is a pretty good place to start and is a nice mix of theory and practice. His approach is a little different to the “Bayes factor” approach that I’ve discussed here, so you won’t be covering the same ground. If you’re a cognitive psychologist, you might want to check out Michael Lee and Eric-Jan Wagenmakers’ (2014) book Bayesian Cognitive Modeling. I picked these two because I think they’re especially useful for people in my discipline, but there’s a lot of good books out there, so look around!