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I really never understood the divide. They are all just tools in our toolbox. It feels like arguing screwdrivers versus hammers. They may not do the same thing,
by imh 11y ago
I really never understood the divide. They are all just tools in our toolbox. It feels like arguing screwdrivers versus hammers. They may not do the same thing, but both can be useful!
- jonahx 11y agoThe author specifically argues against this view as something that obscures the superiority, in most cases, of the Bayesian approach: One downside of this ecumenicalism is a reluctance to ask fundamental questions: having a strong opinion on this matter has gone out of fashion. Who’s to say one statistical philosophy is better than another? Aren’t all statistical philosophies equally valid paths to good data analysis? Frequentism is “true for me”. As in religion, so in statistics. If you criticise a colleague for using p-values when posterior probabilities are clearly more appropriate, it will lead to accusations of being a ‘zealot’ [2] who should stop ‘crusading’ and then later: If Einstein’s theory were easier (as well as being more accurate), teaching Newton’s would be silly. Yet that’s the way most statistics curricula are structured. The only reason statisticians think frequentist ideas are easier is that they are used to them
- imh 11y agoI guess I hate the idea of a "statistical philosophy." It's math for god's sake. Given these axioms, these other things are true. The point about teaching relativistic versus newtonian mechanics is totally different because they're incompatible. One is more correct than the other, which is not the case here. In the stats case, both tools give correct answers to different questions. Maybe I just get frustrated feeling like people want a data analysis silver bullet to teach.
- kgwgk 11y agoI'd say the analogy is more valid than you think. Classical (frequentist) methods are valid as long as the results correspond to the relativistic (Bayesian) ones.
- lottin 11y agoAs far as I know, Bayes came first and Fisher later, so some may say the analogy is the other way round.
- david_ar 11y agoThat's true, but Bayesian methods were handicapped by a lack of computational resources for quite a long time. Modern Bayesian methods, although fundamentally the same, are in practice quite different today compared to a century ago. Frequentist statistics gained popularity because it was more tractable to apply to complex problems until computational technology caught up. Edit: one can argue that frequentist methods are generally approximations to Bayesian methods, much like Newton vs Einstein
- murbard2 11y agoNo it's not "just math". You need to know what questions you're asking. You can't wave this under "do whatever works best" because evaluating "what works best" requires epistemology, and this is precisely what we're discussing. There are some consistency properties of Bayesianism which make it a much better candidate for epistemology than frequentism. Specifically, there are important questions in frequentism for which there are no answers. In particular, the concept of "power of a test" sweeps the prior under the rug without a proper explanation.
- loup-vaillant 11y agoThose "tools" often give different answers to the same questions. Clearly, at least one of them is wrong. And one is probably better than the other. Now if you're tempted to use one or the other in a case by case basis, how are we supposed to choose which is best?