Decreasing AIC while incresing standard errors

I've got a problem chossing the right model. I have a model with various variables (covariables and dummyvariables). I was trying to find the best seize of the model, so I first started in comapring the 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 dumies). When I compute the summary of the model, all effects are absolutely not significant and its std. errors are very high. I was a bit confused, when开发者_如何学Go 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 alot

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I would recommend you also computing AICc and compare the results with AIC. This might be helpful : http://en.wikipedia.org/wiki/Akaike_information_criterion

Sincerely