Bayesian Benefits for the Pragmatic Researcher

The practical advantages of Bayesian inference are demonstrated through two concrete examples. In the first example, we wish to learn about a criminal’s IQ: a problem of parameter estimation. In the second example, we wish to quantify support in favor of a null hypothesis, and track this support as the data accumulate: a problem of hypothesis testing. The Bayesian framework unifies both problems within a coherent predictive framework, where parameters and models that predict the data successfully will receive a boost in plausibility, whereas parameters and models that predict poorly suffer a decline. Our examples demonstrate how Bayesian analyses can be more informative, more elegant, and more flexible than the orthodox methodology that remains dominant within the field of psychology.