A Bayesian analysis lets you see how the posterior varies as a function of the prior, instead of forcing you to pick a prior before you start.

The tighter the range of this function, the more confidence you have in the result.

You can never know anything if you absolutely refuse to have a prior, because that gives division by 0 in the posterior.

What? Maybe in a very specific context where you are modeling joint distributions of people and traits, but that’s barely a critique of the method itself.

it's not a critique of the method