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Mayo comments on Rationality Quotes September 2013 - Less Wrong
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Yeah. The problem is that most scientists seem to still be taught from textbooks that use a Popperian paradigm, or at least Popperian language, and they aren't necessarily taught probability theory very thoroughly, they're used to publishing papers that use p-value science even though they kinda know it's wrong, etc.
So maybe if we had an extended discussion about philosophy of science, they'd retract their Popperian statements and reformulate them to say something kinda related but less wrong. Maybe they're just sloppy with their philosophy of science when talking about subjects they don't put much credence in.
This does make it difficult to measure the degree to which, as Eliezer puts it, "the world is mad." Maybe the world looks mad when you take scientists' dinner party statements at face value, but looks less mad when you watch them try to solve problems they care about. On the other hand, even when looking at work they seem to care about, it often doesn't look like scientists know the basics of philosophy of science. Then again, maybe it's just an incentives problem. E.g. maybe the scientist's field basically requires you to publish with p-values, even if the scientists themselves are secretly Bayesians.
If there was a genuine philosophy of science illumination it would be clear that, despite the shortcomings of the logical empiricist setting in which Popper found himself , there is much more of value in a sophisticated Popperian methodological falsificationism than in Bayesianism. If scientists were interested in the most probable hypotheses, they would stay as close to the data as possible. But in fact they want interesting, informative, risky theories and genuine explanations. This goes against the Bayesian probabilist ideal. Moreover, you cannot falsify with Bayes theorem, so you'd have to start out with an exhaustive set of hypotheses that could account for data (already silly), and then you'd never get rid of them---they could only be probabilistically disconfirmed.
Strictly speaking, one can't falsify with any method outside of deductive logic -- even your own Severity Principle only claims to warrant hypotheses, not falsify their negations. Bayesian statistical analysis is just the same in this regard.
A Bayesian analysis doesn't need to start with an exhaustive set of hypotheses to justify discarding some of them. Suppose we have a set of mutually exclusive but not exhaustive hypotheses. The posterior probability of an hypothesis under the assumption that the set is exhaustive is an upper bound for its posterior probability in an analysis with an expanded set of hypotheses. A more complete set can only make a hypotheses less likely, so if its posterior probability is already so low that it would have a negligible effect on subsequent calculations, it can safely be discarded.
I'm a Bayesian probabilist, and it doesn't go against my ideal. I think you're attacking philosophical subjective Bayesianism, but I don't think that's the kind of Bayesianism to which lukeprog is referring.