Biases in Data Science

In teaching my AP CSP students about the effects of technology on society (specifically when completing's "Tell a Data Story"), I've found that many of my students think that avoiding the causality fallacy (correlation does not equal causation) is enough. But there are so many other ways we can be biased when collecting, analyzing and reporting on data! While it is outside the AP CSP standards to know all of specific kinds of bias, I think a discussion and practice application of identifying some of the more common kinda could help to prepare students for the single select questions with reading passages (SSQRPs) on the AP CSP exam and will make the wiser consumers and producers of content general throughout their lives.

This resource would also tie in well to my previous lesson post on Algorithmic Bias.