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 high-risk prisoners and then see how many 1) people who are paroled and re-offend (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.
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 high-risk prisoners and then see how many 1) people who are paroled and re-offend (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.
 |
| Here, I have set the sliding scale so that there is a broad range for designating a prisoner as "Medium Risk". As such, you have 23% of paroled prisoners landing back in jail and 17% of your unparoled prisoners sitting in jail even though they wouldn't have re-offended. As we expand our range of "significance" (here, prisoners we parole), we increase the possibility of false positives (here, folks who re-offend) but have a smaller amount of false negatives |
 |
| Meanwhile, if you have very stringent standards, you will have fewer false positives (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). |