I've got a problem choosing the right model. I have a model with various variables (covariables and dummy variables). I was trying to find the best size for this model, so I first started by comparing different models with AIC. From this it followed, that the minimum AIC was reached when allowing all variables to stay in the model (with the whole bunch to interact with all dummies). When I compute the summary of the model, all effects are absolutely not significant and the standard errors are very high. I was a bit confused, when comparing the "best" (on AIC) model with a smaller model with any interaction. The smaller model had small standard errors and nice p-values... But the AIC is higher compared to the big model. What might be the problem? Overspecification?
I really need help in this, because I have absolutely no idea how to handle this!
Thanks a lot