Andrew Gelman on the roles of theory and statistics in science

Gelman has an interesting post: The replication crisis and the failure of theory within social psychology, Statistical Modeling, Causal Inference, and Social Science, June 12, 2025.

This seems to be the main point, one that rises above the particular context, which is a body of experiments about social priming:

There’s lots to say about the statistical problems of unreplicated or unreplicable research–indeed, I’ve had a lot to say about the topic myself!–but that’s all just a means to an end. And, as I and others have argued, the fundamental problem with a lot of this bad research is not the bad statistics but rather the bad substantive theory, along with bad connections between theory and data. The bad statistics enables the bad science to appear successful; it does not in itself make the science bad.

To put it another way: bad science analyzed using good statistics does not produce good science; at best, it just makes it harder to be fooled by noise. Conversely, good science can proceed just fine using bad statistics. Good statistics should make good science more efficient and effective, but it’s usually not necessary.