So, I'm doing an interaction model with response vs treatment_type interaction with age+controls(for confounders) with age being continous, say patients ranging from 20 years old to 90 years old. so I have two models. y=age+treatment_type . + . age*treatment_type y=factor(age)+treatment_type . + . factor(age)*treatment_type Basically what I got was that age is highly significant for the continous model. For slight deviations in age, there is a huge effect on of treatment. However, when age is factorized into groups, there is barely any interaction effect. (pvalue not significant) Why? What could be the reason for this. Is the reason why it's so significant for the continous model is that patients simply differ from each other so much, that it seems like age has an effect, but it really doesn't. Afterall, it has no effect when looking at groups. But somehow, within the groups, slight deviations give a large effect.