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Whether or not they should be preferred, there is massive institutional and knowledge-related inertia behind frequentist approaches. Rather than throw out the b

Whether or not they should be preferred, there is massive institutional and knowledge-related inertia behind frequentist approaches. Rather than throw out the baby with the bathwater, throw out all legacy code and retrain millions of scientists in Bayesian, we should consistently urge better use of frequentist approaches. To transition the next generation of scientists to Bayesian will require universities/stats depts/other depts to do so. Have you seen this to be the case right now? Because in my experience of a few different research settings, almost no one I know, of around 50+ researchers, except those passionate about statistics (very rare person indeed) is using Bayesian methodology. How to improve Bayesian knowledge in the world? This has its own set of challenges, as Bayesian thinking is arguably more mathematically challenging for most numerophobic people. IMO, it will take multiple generations of effort to transition everyone over.

0815test 8y ago
> ... 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!
lenticular 8y ago
Frequentist stats just aren't that useful for experimental results because they don't tell folks what they need to know. Even ignoring that, researchers understanding of even basic concepts like P-values is so bad that one might as well throw out everything, baby and all. In one survey [0], 94% of Spanish academic psychologists believed in some form of the inverse probability fallacy. A similar survey in Italy showed similar results. Anecdotally, I can definitely say that the vast majority of American physical scientists I've talked to about this also fell prey to this. With the inverse probability fallacy, one attaches a Bayesian interpretation to P-values, so I don't think training scientists in Bayesian stats would be harder than getting them to use frequentist stats correctly (which is a minefield in comparison). [0] https://www.researchgate.net/publication/280580018_Interpretation_of_the_p_value_A_national_survey_study_in_academic_psychologists_from_Spain https://www.researchgate.net/publication/280580018_Interpret...

0815test 8y ago

> ... 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!

lenticular 8y ago

Frequentist stats just aren't that useful for experimental results because they don't tell folks what they need to know. Even ignoring that, researchers understanding of even basic concepts like P-values is so bad that one might as well throw out everything, baby and all. In one survey [0], 94% of Spanish academic psychologists believed in some form of the inverse probability fallacy. A similar survey in Italy showed similar results. Anecdotally, I can definitely say that the vast majority of American physical scientists I've talked to about this also fell prey to this. With the inverse probability fallacy, one attaches a Bayesian interpretation to P-values, so I don't think training scientists in Bayesian stats would be harder than getting them to use frequentist stats correctly (which is a minefield in comparison). [0] https://www.researchgate.net/publication/280580018_Interpretation_of_the_p_value_A_national_survey_study_in_academic_psychologists_from_Spain https://www.researchgate.net/publication/280580018_Interpret...