Now that you understand conditional probability, you can understand how to apply Bayes' theorem, which is based on conditional probability. It's a very important concept, especially if you're going into the medical field, but it is broadly applicable too, and you'll see why in a minute.
You'll hear about this a lot, but not many people really understand what it means or its significance. It can tell you very quantitatively sometimes when people are misleading you with statistics, so let's see how that works.
First, let's talk about Bayes' theorem at a high level. Bayes' theorem is simply this: the probability of A given B is equal to the probability of A times the probability of B given A over the probability of B. So you can substitute A and B with whatever you want.
