Background: I do not have an formal training in Bayesian statistics (though I am very interested in learning more), but I know enough--I think--to get the gist of why many feel as though they are preferable to Frequentist statistics. Even the undergraduates in the introductory statistics (in social sciences) class I am teaching find the Bayesian approach appealing--"Why are we interested in calculating the probability of the data, given the null? Why can't we just quantify the probability of the null hypothesis? Or the alternative hypothesis? And I've also read threads like these, which attest to the empirical benefits of Bayesian statistics as well. But then I came across this quote by Blasco (2001; emphasis added):


  If the animal breeder is not interested in the philosophical problems associated with induction, but in tools to solve problems, both Bayesian and frequentist schools of inference are well established and it is not necessary to justify why one or the other school is preferred. Neither of them now has operational difficulties, with the exception of some complex cases...To choose one school or the other should be related to whether there are solutions in one school that the other does not offer, to how easily the problems are solved, and to how comfortable the scientist feels with the particular way of expression results. 


The Question: The Blasco quote seems to suggest that there might be times when a Frequentist approach is actually preferable to a Bayesian one. And so I am curious: when would a frequentist approach be preferable over a Bayesian approach? I'm interested in answers that tackle the question both conceptually (i.e., when is knowing the probability of the data conditioned on the null hypothesis especially useful?) and empirically (i.e., under what conditions do Frequentist methods excel vs. Bayesian?). 

It would also be preferable if answers were conveyed as accessibly as possible--it would be nice to take some responses back to my class to share with my students (though I understand some level of technicality is required). 

Finally, despite being a regular user of Frequentist statistics, I am actually open to the possibility that Bayesian just wins across the board.  

Here's five reasons why frequentists methods may be preferred: 


Faster. Given that Bayesian statistics often give nearly identical answers to frequentist answers (and when they don't, it's not 100% clear that Bayesian is always the way to go), the fact that frequentist statistics can be obtained often several orders of magnitude faster is a strong argument. Likewise, frequentist methods do not require as much memory to store the results. While these things may seem somewhat trivial, especially with smaller datasets, the fact that Bayesian and Frequentist typically agree  in results (especially if you have lots of informative data) means that if you are going to care, you may start caring about the less important things. And of course, if you live in the big data world, these are not trivial at all. 
Non-parametric statistics. I recognize that Bayesian statistics does have non-parametric statistics, but I would argue that the frequentist side of the field has some truly undeniably practical tools, such as the Empirical Distribution Function. No method in the world will ever replace the EDF, nor the Kaplan Meier curves, etc. (although clearly that's not to say those methods are the end of an analysis). 
Less diagnostics. MCMC methods, the most common method for fitting Bayesian models, typically require more work by the user than their frequentist counter part. Usually, the diagnostic for an MLE estimate is so simple that any good algorithm implementation will do it automatically (although that's not to say every available implementation is good...). As such, frequentist algorithmic diagnostics is typically "make sure there's no red text when fitting the model". Given that all statisticians have limited bandwidth, this frees up more time to ask questions like "is my data really approximately normal?" or "are these hazards really proportional?", etc. 
Valid inference under model misspecification. We've all heard that "All models are wrong but some are useful", but different areas of research take this more or less seriously. The Frequentist literature is full of methods for fixing up inference when the model is misspecified: bootstrap estimator, cross-validation, sandwich estimator (link also discusses general MLE inference under model misspecification), generalized estimation equations (GEE's), quasi-likelihood methods, etc. As far as I know, there is very little in the Bayesian literature about inference under model misspecification (although there's a lot of discussion of model checking, i.e., posterior predictive checks). I don't think this just by chance: evaluating how an estimator behaves over repeated trials does not require the estimator to be based on a "true" model, but using Bayes theorem does! 
Freedom from the prior (this is probably the most common reason for why people don't use Bayesian methods for everything). The strength of the Bayesian standpoint is often touted as the use of priors. However, in all of the applied fields I have worked in, the idea of an informative prior in the analysis is not considered. Reading literature on how to elicit priors from non-statistical experts gives good reasoning for this; I've read papers that say things like (cruel straw-man like paraphrasing my own) "Ask the researcher who hired you because they have trouble understanding statistics to give a range that they are 90% certain the effect size they have trouble imagining will be in. This range will typically be too narrow, so arbitrarily try to get them to widen it a little. Ask them if their belief looks like a gamma distribution. You will probably have to draw a gamma distribution for them, and show how it can have heavy tails if the shape parameter is small. This will also involve explaining what a PDF is to them."(note: I don't think even statisticians are really able to accurately say a priori whether they are 90% or 95% certain whether the effect size lies in a range, and this difference can have a substantial effect on the analysis!). Truth be told, I'm being quite unkind and there may be situations where eliciting a prior may be a little more straightforward.  But you can see how this is a can of worms. Even if you switch to non-informative priors, it can still be a problem; when transforming parameters, what are easily mistaken for non-informative priors suddenly can be seen as very informative! Another example of this is that I've talked with several researchers who adamantly do not want to hear what another expert's interpretation of the data is because empirically, the other experts tend to be over confident. They'd rather just know what can be inferred from the other expert's data and then come to their  own conclusion. I can't recall where I heard it, but somewhere I read the phrase "if you're a Bayesian, you want everyone to be a Frequentist". I interpret that to mean that theoretically, if you're a Bayesian and someone describes their analysis results, you should first try to remove the influence of their prior and then figure out what the impact would be if you had used your own. This little exercise would be simplified if they had given you a confidence interval rather than a credible interval!


Of course, if you abandon informative priors, there is still utility in Bayesian analyses. Personally, this where I believe their highest utility lies; there are some problems that are extremely hard to get any answer from in using MLE methods but can be solved quite easily with MCMC. But my view on this being Bayesian's highest utility is due to strong priors on my part, so take it with a grain of salt.
