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 start up, 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 high risk prisoners and then see how many 1) people who are paroled and reoffend (Type II error: False negative) versus 2) people who are denied parole but wouldn't have reoffended (Type I error: False positive). When you adjust the risk level (below, in Column 2) and then see your outcomes (below, in column 3), it really does reflect on the balance between power and confidence.
|Meanwhile, if you have very stringent standards, you will have fewer false positive (only 10% of those paroled will re-offend) but then you have a lot more false negatives (people denied parole who wouldn't have re-offended).|