False Models and Post-Data Model Construction

Abstract

This article proposes a method of discounting evidence when linear regression models are constructed after the data have been partially analyzed. The solution parallels a formal decision theoretic analysis of a presimplification problem in which models or model spaces are simplified before observation in order to avoid observation or processing costs. Post-data model construction is interpreted as the data-dependent decision that this pre-simplification is undesirable. The discounting implications of this formal analysis may then be used to police data analyses with less formal post-data model construction.