I do not understand the problem people have with priors in Bayesian methodology. Yes, it is true that a poor choice of prior can affect results. But classical, frequentist techniques incorporate priors implicitly: a flat prior indicating we have no information other than the data. And just as a poor Bayesian prior based on subjective belief can ruin an analysis, a non informative prior implicitly made can be just as catastrophic. And it is truly a rare case when there is absolutely nothing known about a process, and in these cases, a flat prior <i>is</i> the kind of poor prior that these people are so afraid of.