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.
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.