  In a regression context, when the relevant subset of explanatory variables is
uncertain, it is common to use a data-driven model selection procedure.
Classical linear model theory, applied naively to the selected sub-model, may
not be valid because it ignores the selected sub-model's dependence on the
data. We provide an explanation of this phenomenon, in terms of overfitting,
for a class of model selection criteria.
