by Why I am not a Bayesian No matter how atheoretical their inclination, scientists are interested in relations between properties of phenomena, not in lists of readings from dials of instruments that detect those properties … Here as elsewhere, Bayesian philosophy of science obscures a difference between scientists’ problems of hypothesis choice and the problems of prediction that are the standard illustrations and applications of probability theory. In the latter situations, such as the standard guessing games about coins and urns, investigators know an enormous amount about the reality they are examining, including the effects of different values of the unknown factor. Scientists can rarely take that much knowledge for granted. It should not be
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Lars Pålsson Syll considers the following as important: Theory of Science & Methodology
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Why I am not a Bayesian
No matter how atheoretical their inclination, scientists are interested in relations between properties of phenomena, not in lists of readings from dials of instruments that detect those properties …
Here as elsewhere, Bayesian philosophy of science obscures a difference between scientists’ problems of hypothesis choice and the problems of prediction that are the standard illustrations and applications of probability theory. In the latter situations, such as the standard guessing games about coins and urns, investigators know an enormous amount about the reality they are examining, including the effects of different values of the unknown factor. Scientists can rarely take that much knowledge for granted. It should not be surprising if an apparatus developed to measure degrees of belief in situations of isolated and precisely regimented uncertainty turns out to be inaccurate, irrelevant or incoherent in the face of the latter, much more radical uncertainty.
Although Bayesians think otherwise, to me there’s nothing magical about Bayes’ theorem. The important thing in science is for you to have strong evidence. If your evidence is strong, then applying Bayesian probability calculus is rather unproblematic. Otherwise — garbage in, garbage out. Applying Bayesian probability calculus to subjective beliefs founded on weak evidence is not a recipe for scientific progress. It is important not to equate science with statistical calculation or applied probability theory. All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. Statistical models are no substitutes for doing real science. Although Bayesianism has tried to extend formal deductive logic into real-world settings via probability theory, this is not a viable scientific way forward. Choosing between theories and hypotheses can never be a question of inner coherence and consistency. Bayesian probabilism says absolutely nothing about reality.
Rejecting probabilism, Popper not only rejects Carnap-style logic of confirmation, he denies scientists are interested in highly probable hypotheses … They seek bold, informative, interesting conjectures and ingenious and severe attempts to refute them.