Your last sentence is telling. A Bayesian analysis tells you Bayesian things: specifically, this is the most reasonable conclusion one can draw from this data,

Your last sentence is telling. A Bayesian analysis tells you Bayesian things: specifically, this is the most reasonable conclusion one can draw from this data, right now. A frequentist analysis also tells you things, but they are different things. Specifically, frequentists are concerned with...frequencies: if we were to run this procedure many times, here's what we can say about the possible outcomes. There's this persistent meme that all Bayesian methods protect you from multiple comparisons/multiple looks problems. That's not really true. Bayesian methods don't offer any Type I/Type II error control--and why would they, when the notion of these errors doesn't make much sense in a Bayesian framework? You can certainly use Bayesian methods to estimate a parameter's value. However this you cannot repeatedly test your estimates--Bayesian or otherwise--in a NHST-like framework and expect things to work out correctly.