  With the advent of so-called default Bayesian hypothesis tests, scientists in
applied fields have gained access to a powerful and principled method for
testing hypotheses. However, such default tests usually come with a compromise,
requiring the analyst to accept a one-size-fits-all approach to hypothesis
testing. Further, such tests may not have the flexibility to test problems the
scientist really cares about. In this tutorial, I demonstrate a flexible
approach to generalizing one specific default test (the JZS t-test; Rouder et
al., 2009) that is becoming increasingly popular in the social and behavioral
sciences. The approach uses two theoretical results, the Savage-Dickey density
ratio (Dickey and Lientz, 1980) and the technique of encompassing priors
(Klugkist et al., 2005) in combination with MCMC sampling via an easy-to-use
probabilistic modeling package for R called Greta. Through a comprehensive
mathematical description of the techniques as well as illustrative examples,
the reader is presented with a general, flexible workflow that can be extended
to solve problems relevant to his or her own work.
