Bayes’s critical insight was that trying to use the winning and losing tickets we see to figure out the overall ticket pool that they came from is essentially reasoning backward. And to do that, he argued, we need to first reason forward from hypotheticals. In other words, we need to first determine how probable it is that we would have drawn the tickets we did if various scenarios were true. This probability—known to modern statisticians as the “likelihood”—gives us the information we need to solve the problem.
