Barry-Jester, Casselman, & Goldstein's "Should prison sentences be based on crimes that haven't been committed yet?"
This article describes how the Pennsylvania Department of Corrections is using risk assessment data in order to predict recidivism, with the hope of using such data in order to guide parole decisions in the future. So, using data to predict the future is very statsy, demonstrates multivariate modeling, and a good example for class, full stop. However, this article also contains a cool interactive tool, entitled "Who Should Get Parole?" that you could use in class. It demonstrates how increasing/decreasing alpha and beta changes the likelihood of committing Type I and Type II errors. The tool allows users to manipulate the amount of risk they are willing to accept when making parole decisions. As you change the working definition of a "low" or "high" risk prisoner, a visualization will startup, and it shows you whether your parolees stay out of prison or come back. From a statistical perspective, users can adjust the definition of a low, medium, and h...