Important to distinguish Bayesian epistemology (the idea that knowledge comes from induction) from Bayesian techniques in statistics. Karl Popper’s epistemology

Important to distinguish Bayesian epistemology (the idea that knowledge comes from induction) from Bayesian techniques in statistics. Karl Popper’s epistemology (critical rationalism) refutes the former, but does not mind the latter. Popper would agree that Bayesian techniques are useful (and even powerful) tools in statistics and practical problem-solving. But he would say that Bayesian ideas are insufficient to understand how knowledge grows and progresses. From a critical rationalism viewpoint, knowledge grows through conjecture and refutation - the proposal and testing of new explanatory theories. Bayesian statistical approaches can be part of the toolkit to falsify those theories, but they can’t explain how they come about, so they aren’t adequate as an epistemology. Popper’s critical rationalism may not be entirely complete, but it seems far better as a starting point for explaining how we get knowledge (which is what an epistemological framework needs to do) than Bayesian ideas about measuring truth statistically. Major breakthroughs like relativity and quantum entanglement are evidence for this: they required conjectures that at first seemed at odds with what we had observed. Physicists had to follow rational theories down very strange paths before any empirical confirmation, eventually to create new explanations that allowed us to model and predict distant cosmological events that had previously appeared random to us. If anyone is interested in these ideas (which are still fairly new to me; sorry for anything I’m expressing poorly), I highly recommend reading The Beginning of Infinity. It is an exciting, wide-ranging study of Popper’s epistemology by the guy who ‘invented’ quantum computing. It also discusses the ‘many universes’ theory, which is fascinating.