Risk assessment is one of the most common tasks in the criminal justice system, yet most professionals in this field receive little to no formal training in statistical techniques for predicting dichotomous outcomes, such as recidivism. The purpose of this special issue was to help fill this gap in training and resources. We wanted to make some of the latest statistical issues and advances in predicting recidivism accessible to the readership of Criminal Justice and Behavior. In this introductory paper, we briefly describe the seven articles in this issue. The first three articles provide primers on topics (statistics to assess predictive accuracy, the E/O Index [Expected/Observed], and mediation analyses, respectively) in a way that is meant to be understandable to clinicians and researchers. The next two articles describe and compare different statistics for assessing change over time. The last two articles explore limitations of currently used recidivism analyses (Area Under the Curve [AUCs], Harrell’s C, Cox and logistic regression). We hope this issue will serve as a helpful resource for those who conduct or consume research on predicting recidivism.