David Papineau on Bayesian Analysis:

The vindication of Bayesian thinking is not yet complete. Perhaps unsurprisingly, many mainstream university statistics departments are still unready to concede that they have been preaching silliness for over a century. Even so, the replicability crisis is placing great pressure on their orthodoxy. Since the whole methodology of significance tests is based on the idea that we should tolerate a 5 per cent level of bogus findings, statistical traditionalists are not well placed to dodge responsibility when bogus results are exposed.

Some defenders of the old regime have suggested that the remedy is to “raise the significance level” from 5 per cent to, say, 0.1 per cent — to require, in effect, that research practice should only generate bogus findings one time in a thousand, rather that once in twenty. But this would only pile idiocy on stupidity. The problem doesn’t lie with the significance level, but with the idea that we can bypass prior probabilities. No sane recipe can ignore prior probabilities when telling you how to respond to evidence. Yes, a theory is disconfirmed if it makes the evidence unlikely and is supported if it doesn’t. But where that leaves us must also depend on how probable the theory was to start with. Thomas Bayes was the first to see this and to understand what it means for probability calculations. We should be grateful that the scientific world is finally taking his teaching to heart.

Read more here.