It seems to me that the strictly Popperian view of science (all models start off as being infinitely improbable, hence no amount of supporting evidence will ever make them probable) is untenable in practice. Every working scientists knows that models that make predictions that are verified by observations do gain in credibility. This of course is automatically reflected in the Bayesian model comparison framework. Most people would probably agree that the Bayesian approach answers the right question (as opposed to p-values!), although it does seem desirable to have an absolute scale for model quality in a Bayesian sense (rather than a relative scale as in model comparison). This is what Bayesian doubt tries to achieve (in this article and this), although the notion remains very much work in progress...