Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation

For the published version of the article, see: Heck, D. W., Moshagen, M., & Erdfelder, E. (2014). Correspondence concerning this article should be addressed to Daniel W. Heck, Department of Psychology, School of Social Sciences, University of Mannheim, Schloss EO 254, D-68131 Mannheim, Germany. FISHER INFORMATION APPROXIMATION 2 Abstract The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection. Unlike information criteria such as AIC or BIC, it has the advantage of taking the functional form of a model into account. Unfortunately, FIA can be misleading in finite samples, resulting in an inversion of the correct rank order of complexity terms for competing models in the worst case.