The problem with frequentism and the null ritual is that it makes statistics easier (just do this one test, read this one number) and renders some kinds of mistakes somewhat harder (publishing a single false positive).
At the same time, it makes some bad mistakes much easier, most notably ignoring power, false positives, the garden of forking paths, type S errors and publication bias.
The inherent problem is that the null model is not what people assume it is, and the method (or at least the established canon of approaches) don't make you think about it.
If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them.
This means that even non-competent bayesians probably have a better contribution to cumulative science.
Frequentist statistics does not force you to accept any hypothesis test with a result p < 0.05 as definitive proof of something. It does not forbid considering prior probability of a result. It just doesn't formalize the consideration of prior probabilities because it is hard to distill this consideration into a formal recipe.
Everyone agrees that Bayes' Rule is valid and important, the question is when and how best to use it.
> If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them.
You're not forced to do those things well. Any scientific method can be cargo culted.
The problem that I care about is not whether frequentist statistics can be taught and used well. They can, and I try in my teaching to do so.
The problem is that empirically, frequentist statistics is a fig leaf for a ton of extremely problematic work. And pushing bayesian thinking is currently our best chance to fix this, because it's easier to do a shift in the mental framework than to fix the perception of an existing framework.
Bayesian methodology benefits from having relatively much more statistically sophisticated practitioners, which leads to an optimism bias when we imagine how it would scale up.
I agree w/ the parent poster than the fundamental issue is probability: if you are talking w/ people w/o background in stats, you will have a really hard time to go beyond a true/false statement.
Moreover, one of the most effective (in $ terms) application of statistics in recent times is A/B testing. While you can do "Bayesian A/B testing", the basic methodology is fundamentally frequentist. Mistakes there can be hedged through better tooling / UX (to avoid peeking, etc.), as effectively as using Bayesian statistics.
Ok, that explains why people bother spend so much energy badmouthing frequentism, but it's a very bad framing anyway, bothering a lie. Frequentism is not the null ritual. In fact, it's almost completely compatible with bayesianism, the one large difference being the freedom to set priors before doing your analysis.
If the article was titled "It's time to stop teaching the null ritual to scientists", nobody would even disagree.
This may be true of "frequentism" in a strict sense, but many statistical methods in common use that are often described as such (including, arguably, NHST) are not consistent with reasonable versions of the likelihood principle https://en.wikipedia.org/wiki/Likelihood_principle . In a sense, one might feasibly argue that these methods are not even properly frequentist.
There will always be no instances where individuals err in the way they apply statistical methods. But that doesn’t mean that there is no value in moving the “default” in statistics to a more intuitive methodology from one that is so obtuse that it’s common for relatively advanced practitioners to stumble over it
It's possible that it can happen the other way around as well, but my impression is that it happens less often.