Which reasons support the preference for simpler models in variable selection?

The preference for simpler models in variable selection is primarily built on several important factors that enhance interpretability and generalizability.

One key reason favoring simpler models is that they are easier to explain. When models involve fewer variables and simpler relationships, stakeholders—including decision-makers who may not have a statistical background—can understand the model's findings and implications more readily. This transparency facilitates communication about results and fosters trust among users of the model.

Additionally, simpler models often perform better on out-of-sample data—this is crucial because it reflects how well the model will generalize to new, unseen data. Complex models, with many parameters, are more prone to fitting the noise in training data rather than capturing the underlying relationships. This tendency can lead to overfitting, where the model performs well on the training data but fails to predict accurately on new data.

The combined impact of these factors, including ease of explanation and enhanced predictive performance on unseen data, underscored the rationale for preferring simpler models in the context of variable selection. Thus, the culmination of all these reasons underscores the importance of adopting a simpler approach when building predictive models, making the choice of "all the above reasons" appropriate.