A/B testing is local optimization. It should only be done on a mature(-ish) product when you have given up on finding a global minimum. Running experiments and A/B tests are popular because it is _guaranteed_ to give you signal. If you have a large engineering team and you're not sure how to filter the quality of results, gating everything through A/B tests is a well understood methodical way to ensure only positive…
> Running experiments and A/B tests are popular because it is _guaranteed_ to give you signal. If you have a large engineering team and you're not sure how to filter the quality of results, gating everything through A/B tests is a well understood methodical way to ensure only positive work makes it way through. It's almost guaranteed to ensure only false positive work makes its way through. If you're picking 0.05 as…
A/A tests do test your methodology as you said. But they do not fix a p-value one order of magnitude higher than it should be. (And yeah, I'm aware you know that, but your comment places them on the same context, so it got misleading.)