Regarding Bayes, you might like my essay on the topic, especially if you have statistical training.

I guess this sounds heretical, but I don't understand why Bayes theorem is placed on such a pedestal here. I understand Bayesian statistics, intuitively and also technically. Bayesian statistics is great for a lot of problems, but I don't see it as always superior to thinking inspired by the traditional scientific method.

I know a few answers to this question, and I'm sure there are others. (As an aside, these foundational questions are, in my opinion, really important to ask and answer.)

1. What separates scientific thought and mysticism is that scientists are okay with mystery. If you can stand to not know what something is, to be confused, then after careful observation and thought you might have a better idea of what it is and have a bit more clarity. Bayes is the quantitative heart of the qualitative approach of tracking many hypotheses and checking how concordant they are with reality, and thus should feature heavily in a modern epistemic approach. The more precisely and accurately you can deal with uncertainty, the better off you are in an uncertain world.
2. What separates Bayes and the "traditional scientific method" (using scare quotes to signify that I'm highlighting a negative impression of it) is that the TSM is a method for avoiding bad beliefs but Bayes is a method for finding the best available beliefs. In many uncertain situations, you can use Bayes but you can't use the TSM (or it would be too costly to do so), but the TSM doesn't give any predictions in those cases!
3. Use of Bayes focuses attention on base rates, alternate hypotheses, and likelihood ratios, which people often ignore (replacing the first with maxent, the second with yes/no thinking, and the latter with likelihoods).
4. I honestly don't think the quantitative aspect of priors and updating is that important, compared to the search for a 'complete' hypothesis set and the search for cheap experiments that have high likelihood ratios (little bets).

I think that the qualitative side of Bayes is super important but don't think we've found a good way to communicate it yet. That's an active area of research, though, and in particular I'd love to hear your thoughts on those four answers.

Hi Less Wrong. I found a link to this site a year or so ago and have been lurking off and on since. However, I've self identified as a rationalist since around junior high school. My parents weren't religious and I was good at math and science, so it was natural to me to look to science and logic to solve everything. Many years later I realize that this is harder than I hoped.

Anyway, I've read many of the sequences and posts, generally agreeing and finding many interesting thoughts. It's fun reading about zombies and Newcomb's problem and the like.

I guess this sounds heretical, but I don't understand why Bayes theorem is placed on such a pedestal here. I understand Bayesian statistics, intuitively and also technically. Bayesian statistics is great for a lot of problems, but I don't see it as always superior to thinking inspired by the traditional scientific method. More specifically, I would say that coming up with a prior distribution and updating can easily be harder than the problem at hand.

I assume the point is that there is more to what is considered Bayesian thinking than Bayes theorem and Bayesian statistics, and I've reread some of the articles with the idea of trying to pin that down, but I've found that difficult. The closest I've come is that examining what your priors are helps you to keep an open mind.