Analogy between (a) model checking in Bayesian statistics, and (b) the self-correcting nature of science.

This came up in a discussion thread a few years ago. In response to some thoughts from Danielle Navarro about the importance of model checking, I wrote:

This makes me think of an analogy between the following two things:

– Model checking in Bayesian statistics, and

– The self-correcting nature of science.

The story of model checking in Bayesian statistics is that the fact that Bayesian inference can give ridiculous answers is a good thing, in that, when we see the ridiculous answer, this signals to us that there’s a problem with the model, and we can go fix it. This is the idea that we would rather have our methods fail loudly than fail quietly. But this all only works if, when we see a ridiculous result, we confront the anomaly. It doesn’t work if we just accept the ridiculous conclusion without questioning it, and it doesn’t work if we shunt the ridiculous conclusion aside and refuse to consider its implications.

Similarly with the self-correcting nature of science. Science makes predictions which can be falsified. Scientists make public statements, many (most?) of which will eventually be proved wrong. These failures motivate re-examination of assumptions. That’s the self-correcting nature of science. But it only works if individual scientists do this (notice anomalies and explore them) and it only works if the social structure of science allows it. Science doesn’t self-correct if scientists continue to stand by refuted claims, and it doesn’t work if they attack or ignore criticism.

In short, science is self-correcting, but only if “science”—that is, the people and the institutions of science—do that correction.

Similarly, statistical methods are checkable, but only if the users of these methods actually check them, and only if the developers of these methods develop methods for users to perform these checks. Which is where I come in, as a methodologist.

As Thomas Bayes famously said, with great power comes great responsibility.