Conditional Probability explained

Before it happened, I would have said slim. But since it happened, 100%.

—Lawrence Fishburne, CSI, on the chances of being hit in the head by a tortoise dropped by a bird of prey.

(This goes well with Ted Bunn’s exegesis of the Daily Show’s brief foray into probability theory for their segment filmed at CERN.)

I know this is a tired topic, but I am unable to resist using this as an opportunity to slag off Stanley Fish’s idiotic attempt to equate “faith” in religion with “faith” in science. In both cases, we are talking about conditional probability, P(hypothesis | information ), which is read as “the probability of the hypothesis given the information”. I suppose that when the religious discuss “faith” in science, they are referring to the fact that something needs to go on the right side of the bar — all probabilities are conditional on something. But a crucial difference between religion and science is that the religious only put a couple of things on the right side: the words of a holy book (and don’t ask me why one should choose one book over another), or just the effects of some vaporous conversion experience which leaves all such probabilities as tautologies — god exists since I know god exists. For science, however, we get to condition our probabilities on, well, pretty much anything and everything. And the more we learn, the better it gets.