The useless answer is that they both do different things, so it depends what of those things you want :) One aspect of frequentist techniques that perhaps othe

The useless answer is that they both do different things, so it depends what of those things you want :) One aspect of frequentist techniques that perhaps others haven't emphasised so much, is that they tend to give guarantees about expected behaviour which hold uniformly over all possible values of the unknown parameters. Whereas the Bayesian approach, the guarantees you obtain will only hold in an 'averaged-out' sense over the prior distribution you specify. If you're a bit paranoid and you want a probabilistic bound on what might happen in the worst case, you might sometimes find the former a little more comforting than the latter. In particular if you don't have much data, the influence of the choice of prior will be bigger and so the distinction will matter more. Hope that helps, and that any stats PhDs will correct me if I've over-simplified things here.