Abstract
Information‐theoretic approaches to model selection, such as Akaike's information criterion (AIC) and cross validation, provide a rigorous framework to select among candidate hypotheses in ecology, yet the persistent concern of overfitting undermines the interpretation of inferred processes. A common misconception is that overfitting is due to the choice of criterion or model score, despite research demonstrating that selection uncertainty associated with score estimation is the predominant influence. Here we introduce a novel selection rule that identifies a parsimonious model by directly accounting for estimation uncertainty, while still retaining an information‐theoretic interpretation. The new rule, which is a modification of the existing one‐standard‐error rule, mitigates overfitting and reduces the likelihood that spurious effects will be included in the selected model, thereby improving its inferential properties. We present the rule and illustrative examples in the context of maximum‐likelihood estimation and Kullback‐Leibler discrepancy, although the rule is applicable in a more general setting, including Bayesian model selection and other types of discrepancy.