I’m sure there are creative ways to misuse bayesian statistics, although I think it is harder to hide your intentions as you do that. With frequentist approaches your intentions become obscure in the whole mess of computations and at the end of it you get to claim this is a simple “objective” truth because the p value shows < 0.05. In bayesan statistics the data you put into it is front and center: The chances of my theory being true given this data is greater than 95% (or was it chances of getting this data given my theory?). In reality most hoaxes and junk science was because of bad data which didn’t get scrutinized until much too late (this is what Gould did).
But I think the crux of the matter is that bad science has been demonstrated with frequentists and is now a part of our history. So people must either find a way to fix the frequentist approaches or throw it out for something different. Bayesian statistics is that something different.
> "The chances of my theory being true given this data is greater than 95% (or was it chances of getting this data given my theory?)"
The first statement assumes that parameters (i.e. a state of nature) are random variables. That's the Bayesan approach. The second statement assumes that parameters are fixed values, not random, but unknown. That's the frequentist approach.
