[abeppu]

I think the weird thing is that a bunch of people in tech understand this well _with respect to tech_, but often fall into the same p-value trap when reading about science.

If you're working with very large datasets generated from e.g. a huge number of interactions between users and your system, whether as a correlation after the fact, or as an A/B experiment, getting a statistically significant result is easy. Getting a meaningful improvement is rarer, and gets harder after a system has received a fair amount of work.

But then people who work in these big-data contexts can read about a result outside their field (e.g. nutrition, psychology, whatever), where n=200 undergrads or something, and p=0.03 (yay!) and there's some pretty modest effect, and be taken in by whatever claim is being made.