References showing that, in general, detecting interactions in linear and logistic regression models requires larger samples sizes than main effects I have always been told that we need larger sample sizes to obtain the same power compared to main effects. Previous posts here confirm that:
Why is power to detect interactions less than that for main effects?
Do you know by heart any published references (e.g., peer-reviewed articles, book chapters)?

Regression and Other Stories by Gelman, Hill, and Vehtari has a discussion of this issue in Section 16.4. Gelman has also discussed it on his blog a few times, with R simulation code here. This is fairly similar to what is in the book.
They argue that it takes 4x the sample size to estimate an interaction that is the same size as the main effect, and 16x for an interaction that is half the size of the main effect.
