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> ... We should consistently urge better use of frequentist approaches.

Many "frequentist" methods can be rephrased as heavily-simplified special cases of the Bayesian approach. Most easily, by assuming a flat prior distribution for the relevant parameters (which is basically an artifact of the parameterization you choose anyway) you can assert that any MLE-based approach will yield a correct Bayesian posterior mode, which in turn is an optimal Bayes-estimator, assuming a constant loss function.

The limits of this whole approach are fairly clear, of course - for one thing, Bayesian stats is generally based on working with the entire posterior distribution, not merely a point estimate of it - and there are good reasons for this. But the basic point stands, and many "tweaks" on the basic frequentist approach can in turn be justified in Bayesian terms. This is not to say that frequentist statistics is all that we'll ever need, but merely pointing out that this whole argument of "we should work with what we have, and focus on making sure that frequentist approaches are put to good use" actually sounds rather vacuous. It's correct in a very limited sense, and hardly something that Bayes proponents are unaware of!




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