These mechanics include the use of a variety of statistical recipes and the correct programming of these recipes using a statistics computer package. Conditional inference . All Bayesian inferential conclusions are made conditional on the observed data. Unlike the traditional approach, one need not be concerned with datasets other than the one that is observed. There is no need to discuss sampling distributions using the Bayesian approach.
Inferential conclusions are understandable .
These inferential conclusions are generally the ones that seem intuitive for the students. In contrast, traditional inferential conclusions are frequently misstated. One recipe . Bayes' rule is really the only inferential method that needs to be taught. Once Bayes' rule is used to compute the posterior distribution, the student just needs to summarize this probability distribution to make inferences.