I ask the question based on a current case, but I would really appreciate a general answer, because it has been bugging me for some time:
I'm running regressions with interaction effects. How do I test if the interaction is significant?
Option A: I look at the interaction coefficients. If they are significant, the interaction is significant.
Option B: I run two regression models: One with all main effects and one with the main effects and interaction terms. If the explanatory power of the interaction model is significantly higher, I interpret the interaction. (e.g., comparing the two models with the anova() function in R; running an F test)
Many of my colleagues choose option A, but I seem to recall that my statistics instructor insisted that option B is preferable.
This question has become pertinent, because I have some models where the interaction term is significant, but the explanatory power of the models with and without the interaction is not significantly different.