I don't even get what the conflict is between Bayesian and Frequentist statistics. Too me it seems like Bayesian statistics is useful for some set of use cases, and frequentist statistics for another set.
Frequentist hypothesis testing falls somewhere in the middle, and as long as it's not over-interpreted is fine as it is, with the advantage being it's easy to do. Though the downside is that it's not easy to interpret.
The downside of Bayesian statistics, in my experience, is that it's really hard to teach it to less-than-very-bright people, even in the academy. I mean, those who don't even understand what "falsifying a null-hypothesis" means (and doesn't mean) will have a very hard time doing Bayesian analysis properly.
In a more advanced setting, though, Bayesian approaches provide a much better tool for comparing a set of alternative hypotheses. But it requires that users understand the math, and are not just following some script, and also that those involved are willing to provide their priors before evaluating the data.
I think they're suggesting that the problem in science is that it's become accounting and metric driven, just like the rest of the world.
Is this an actual stereotype that Bayesians are right wing? It doesn't seem at all political to me, but I guess I've never met someone who self identified as a Bayesian either.
The author of the blog post makes a similar argument in the comments section about randomized controlled trials - that they're good for "further down the line" efficacy studies, not scientific discovery per se[0].
I don't think philosophical stance to statistics correlates very strongly to other political views. There are maybe some undercurrents regarding epistemology (Bayesians being relativists/antipositivists and frequentists being more positivist about "the truth") which maybe would be a slight left lean for Bayesians. And the father of frequentism/NHST was pretty HC right wing, but I don't think that means much either.
I'm more of a Bayesian in interpretation of statistics and epistemology, and I'm pretty far left. N=1 of course.
Neoliberalism refers to a social order and doesn't have much to do with probability and statistics.
Neoliberal means roughly the economic (and social) system/ideology we live in currently. A system that emphasizes relatively weakly regulated ("free") market and market-like structures as the way of allocating resources and production.
This can be contrasted to the preceding systems that had more regulation of markets and planned resource production and allocation. Roughly Keynesian or social democratic economics, with their associated view of social organization.
Okay, so what does this have to do with science, exactly? Back in the days of the original liberalism, often with no regulation of markets whatsoever, science indeed was a tinkerer's world, with ethics and reputation being at the core of an essentially rudderless and bottom-up endeavour. And it worked very well, but then matured and became more bureaucratic, formalized, which is a trade-off, yadda yadda. How is this latter state "neoliberal"? Are you saying this wasn't already happening in the 1970s when changes/reforms that can be described as neoliberal started happening, and then was caused by those reforms?
The most influential change has been the increase in competition for resources in academia, especially via grants and similar competitive funding (to both researchers/research teams and to universities themselves) [1]. The idea that competition breeds efficiency is a core tenet of (neo)liberalism.
There has been a concurrent shift towards bibliometric assesment of researchers and institutions, to a large part to have metrics for the competition.
In general this is largely an application of the (neoliberal) New Public Management [2] model applied to academia and science.
Before this shift funding was based mostly on budgets akin to how e.g. schools or (public) healthcare and police are funded. Academics were mostly just hired to a (permanent contract) job when there was an opening. Anecdotally, my supervising professor was tenured almost straight after he got his PhD in the 1970s. And he didn't really have to apply for competitive funding until 2000's or so. And this was more or less the norm back then.
In angloamerican countries the neoliberal turn started already in the 1970s. Although there was also a contemporary explosion in funding of science much due to the role of scientific and technological progress in the Cold War.
The degree inflation likely plays a role too, and in research this shows as more PhD students who have to churn out papers to get their degrees (which is beneficial to the universities as they are assessed on how many affiliated publications they output). There's of course plenty of quality PhD-level research but it's also quite obvious that the quality is also affected by the students still essentially learning the ropes.
Academia's, science's and higher education's societial position and "clout" was quite different and smaller in the era of classical liberalism (roughly until WW1), and public funding had a lot smaller role.
It's of course totally arguable that competition leads to more efficiency. However, the academic world doesn't really have a natural market and establishing comparative value of different lines of research, academic institutions or individual researchers is very difficult. And due to this the resource allocation is done largely based on quantity of scholarly outputs (papers and PhD degrees) and salesmanship. These of course are very easy to play, and those who don't engage in the play tend to not survive. And it adds huge overheads.
