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Downvote me all you want. Bayesianism is misapplied much more frequently than frequentism. It just makes it way too easy to fudge p values. Sorry not sorry.
by therobots927 7mo ago
Downvote me all you want. Bayesianism is misapplied much more frequently than frequentism. It just makes it way too easy to fudge p values. Sorry not sorry.
- jmalicki 7mo agoI do always laugh when I see a Bayesian object to p-values, then use a Bayesian procedure that is mathematically identical to treating p values as posterior probabilities. Just saying the word "Bayesian" doesn't actually make it different
- kgwgk 7mo agoIt’s mathematically identical but conceptually different. The things that go into the calculation are different, the numbers that get out of the calculation mean different things. Laughing is healthy though.
- jmalicki 7mo agoYou can just shortcut all of that if you're a Bayesian and just plain say "p-values are posterior probabilities under a uniform (improper) prior" and save everyone a lot of time. And if you're doing that, don't care complain that p-values can be misinterpreted, because you're basically just laundering the misinterpretation of p-values. Sure, you are mathematically pure because you made an initial assumption that it can be so, rather than being confused, but the end result is the same.
- kgwgk 7mo agoI have no interest in laundering the misinterpretation of p-values. I don’t know whose time is saved, frequentists don’t care about the Bayesian interpretation anyway and Bayesians don’t need to restrict themselves to a particular prior. The fact that in some cases you can choose one particular prior to get a numerical value for one probability that is equal to the numerical value for the probability of a completely different thing calculated by someone else doesn’t help anyone much. Bayesians can get the same result on their own if they want.
- jpfromlondon 7mo agoshow your model