How statistics is used to crush (scientific) dissent.

Lakeland writes:

When we interpret powerful as political power, I think it’s clear that Classical Statistics has the most political power, that is, the power to get people to believe things and change policy or alter funding decisions etc… Today Bayes is questioned at every turn, and ridiculed for being “subjective” with a focus on the prior, or modeling “belief”. People in current power to make decisions about resources etc are predominantly users of Classical type methods (hypothesis testing, straw man NHST specifically, and to a lesser extent maximum likelihood fitting and in econ Difference In Difference analysis and synthetic controls and robust standard errors and etc all based on sampling theory typically without mechanistic models…).

The alternative is hard: model mechanisms directly, use Bayes to constrain the model to the reasonable range of applicability, and do a lot of computing to get fitted results that are difficult for anyone without a lot of Bayesian background to understand, and that specifically make a lot of assumptions and choices that are easy to question. It’s hard to argue against “model free inference procedures” that “guarantee unbiased estimates of causal effects” and etc. But it’s easy to argue that some specific structural assumption might be wrong and therefore the result of a Bayesian analysis might not hold…

So from a political perspective, I see Classical Stats as it’s applied in many areas as a way to try to wield power to crush dissent.

My reply:

Yup. But the funny thing is that I think that a lot of the people doing bad science also feel that they’re being pounded by classical statistics.

It goes like this:
– Researcher X has an idea for an experiment.
– X does the experiment and gathers data, would love to publish.
– Because of the annoying hegemony of classical statistics, X needs to do a zillion analyses to find statistical significance.
– Publication! NPR! Gladwell! Freakonomics, etc.
– Methodologist Y points to problems with the statistical analysis, the nominal p-values aren’t correct, etc.
– X is angry: first the statistical establishment required statistical significance, now the statistical establishment is saying that statistical significance isn’t good enough.
– From Researcher X’s point of view, statistics is being used to crush new ideas and it’s being used to force creative science into narrow conventional pathways.

This is a narrative that’s held by some people who detest me (and, no, I’m not Methodologist Y; this might be Greg Francis or Uri Simonsohn or all sorts of people.) There’s some truth to the narrative, which is one thing that makes things complicated.

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