While I like Bayesian statistics it is NOT a substitute for maximum likelihood in many situations.
Additionally, some of his criticism of NHST is unfair because he criticizes weaknesses Bayesian also has. In the part where he gives the example of the pollster and how uncertain the p value would be because you have to incorporate sample design- well, you have to do that in Bayesian stats too.
Statistics is a big field. Obviously people should expand their tool set, and maybe Bayesian is underused, but that doesn't mean Bayesian stats are right for every experiment and experimental design.
Maximum likelihood estimation falls out of Bayesian statistics with the right utility function. In practice though, that's often not the utility function you want, hence general Bayesian statistics.
Additionally, some of his criticism of NHST is unfair because he criticizes weaknesses Bayesian also has. In the part where he gives the example of the pollster and how uncertain the p value would be because you have to incorporate sample design- well, you have to do that in Bayesian stats too.
Statistics is a big field. Obviously people should expand their tool set, and maybe Bayesian is underused, but that doesn't mean Bayesian stats are right for every experiment and experimental design.