The article discusses how traditional model selection methods can lead to underestimating uncertainty and overly optimistic results. The researchers developed a new approach that considers the uncertainty introduced by model selection, leading to more accurate estimations. They also compared different model averaging schemes and introduced new model selection criteria. The methods were tested with real data applications.