In my last post, I covered a pitfall of data analysis where two independent factors may appear to be correlated with each other. Today I’ll cover another common pitfall when interpreting what your data are telling you. This one is particularly troublesome because most peoples’ intuition is so clear, but so wrong. To illustrate, I’ll use an example of our student risk model. We have a model that can be applied to students and it’s 85% accurate in classifying students into groups of “will pass this class” (classified not risky) and “will fail this class” (classified risky). Boiling it down to two outcomes like red light / green light understates the complexity, but once the math is done, it really is that easy to use. Pretty awesome, right? Yeah, we get that a lot. Now, let’s say you’re teaching a class where you know 90% of students pass, but 10%…