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AlexMennen comments on Case study: abuse of frequentist statistics - Less Wrong
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I too would like to see a good explanation of frequentist techniques, especially one that also explains their relationships (if any) to Bayesian techniques.
Based on the tiny bit I know of both approaches, I think one appealing feature of frequentist techniques (which may or may not make up for their drawbacks) is that your initial assumptions are easier to dislodge the more wrong they are.
It seems to be the other way around with Bayesian techniques because of a stronger built-in assumption that your assumptions are justified. You can immunize yourself against any particular evidence by having a sufficiently wrong prior.
EDIT: Grammar
The ability to get a bad result because of a sufficiently wrong prior is not a flaw in Bayesian statistics; it is a flaw is our ability to perform Bayesian statistics. Humans tend to overestimate their confidence of probabilities with very low or very high values. As such, the proper way to formulate a prior is to imagine hypothetical results that will bring the probability into a manageable range, ask yourself what you would want your posterior to be in such cases, and build your prior from that. These hypothetical results must be constructed and analyzed before the actual result is obtained to eliminate bias. As Tyrrell said, the ability of a wrong prior to result in a bad conclusion is a strength because other Bayesians will be able to see where you went wrong by disputing the prior.