I am only just learning about this stuff, but there are several things in this article that seem incorrectly explained. Conceptual clarity is paramount to me,

I am only just learning about this stuff, but there are several things in this article that seem incorrectly explained. Conceptual clarity is paramount to me, so it drove me a little crazy! > Bayes’ theorem is a method for calculating the validity of beliefs (hypotheses, claims, propositions) based on the best available evidence (observations, data, information). Bayes theorem is a statement about probability, not "validity." This description makes it sound like Bayes theorem is a function BT(belief, evidence) = validity of belief. But it's not like that at all. Probability is a way of measuring uncertainty. Things are uncertain for two main reasons: either we can't observe them directly ("do I have this disease or not?") or they haven't happened yet ("what side will this coin flip land on?"). (If you believe in a deterministic universe, the second is just a special case of the first.) The "beliefs" (aka priors/posteriors) in Bayes theorem are statements of probability. To use the article's example, if it is claimed that 1% of the population has a certain disease, your "belief" or "prior" is that P(I have the disease) = 0.01. The article seems to get confused and think that the "belief" here is "I have the disease." Bayes theorem doesn't tell you about "the probability that a belief is true" like the article says, the belief is a probability. It also doesn't tell you if your belief is "valid." Bayes theorem takes your existing belief about the probability of something and gives you a new probability that incorporates some evidence you observed.