In linear regression, if the assumptions of normally distributed residuals and homogenous residuals are broken, incorrect standard errors can be calculated. This can lead to some predictors appearing statistically significant when they have no effect on the response variable.
But in information theoretic model selection, there is no concept of 'statistically significant predictors'. Basically, the models that result in the smallest residual variation are considered 'the best'.
So, does this mean that in information theoretic model selection, incorrect standard errors are not a problem?