Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of this talk is to help explain some of the basic terminology of Bayesian statistics (prior distributions, posterior distributions, credible intervals, conjugacy, etc.), some options regarding software to perform the analyses, and how interpretations of results change in this new paradigm. We’ll use the R software language to run some examples of multiple linear regression and probit regression using the bayesm package that will illustrate these concepts. Hopefully you'll come away with a better concept of what these researchers are doing next time you read one of their papers and possibly an interest in performing them yourself.