Hi Statlists,

Hope this post finds you well. May I know why stratification seems to be less preferable than adding an interaction term into a model straight away? is it because p-value derived from each subgroup tends to be meaningless once we stratify data into groups, as the power analysis maybe impeded by the sample size of each strata itself? In this case, based on the reason given above, am I right to say that interaction term is still more preferable than the stratification itself? It is probably because there is a need in retaining the sample size ? for example, an interaction was found between A and B on C. the interaction between A and B3 on C was observed to be significant in a regression analysis ,which took Interaction term term into account . But when I just looked at the association between A and C while stratifying data into three B groups - B1 (n=100),B2 (n=75) and B3(n=45), the effect between A and B3 on C became insignificant. Why is this so? Is it probably due to the change in sample size ?

Any input and comments are much appreciated.

Thank you for the clarification in advance.

Em