Model Selection, Simplicity, and Scientific Inference

The Akaike Information Criterion can be a valuable tool of scientific inference. This statistic, or any other statistical method for that matter, cannot, however, be the whole of scientific methodology. In this paper some of the limitations of Akaikean statistical methods are discussed. It is argued that the full import of empirical evidence is realized only by adopting a richer ideal of empirical success than predictive accuracy, and that the ability of a theory to turn phenomena into accurate, agreeing measurements of causally relevant parameters contributes to the evidential support of the theory. This is illustrated by Newton's argument from orbital phenomena to the inverse-square law of gravitation. Malcolm Forster and Elliott Sober (1994) have appealed to a concept of predicted fit to defend the Akaike Information Criterion as a criterion for model selection in scientific inference. Given assumptions about errors in the data, fit of a model to the data is not always a good