Sometimes we suspect a die is loaded, a new medicine works better than an old one, or that career choice has nothing to do with gender. In each case, our data are counts in categories — and we need a fair way to decide if what we see matches what we expected. This chapter gives you that tool: the chi-square test. It may sound fancy, but the idea is beautifully simple: compare what you saw to what you would expect if a belief were true, and see if the gap is too big to blame on chance.