Weather prediction is data. This makes weather data-related stories and examples highly relatable. The Washington Post published an interactive article t hat shows how accurate weather predictions are for a given city in the United States. This means that we, stats instructors, can use this page to provide a geographically personalized lesson on weather prediction, the limitations of data, and why predictions about the future are only as good as the consistency of the past. I also like this example because it isn't terribly mathy and encourages statistical literacy.  Kommenda and Stevens, writing for the Washington Post, recently shared a story on the accuracy of weather predictions based on time away from the target day. Here, the DV is prediction accuracy, operationalized using the difference between predicted and actual high temperature. You could always ask your students how they would operationalize weather...or maybe some weather matters more than others? Folks in Erie...

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...