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Statistical Inference After Model Selection

NCJ Number
231366
Journal
Journal of Quantitative Criminology Volume: 26 Issue: 2 Dated: June 2010 Pages: 217-236
Author(s)
Richard Berk; Lawrence Brown; Linda Zhao
Date Published
June 2010
Length
20 pages
Annotation
This paper examines the procedures and processes used for model selection in criminology.
Abstract
Conventional statistical inference requires that a model of how the data were generated be known before the data are analyzed. Yet in criminology, and in the social sciences more broadly, a variety of model selection procedures are routinely undertaken followed by statistical tests and confidence intervals computed for a "final" model. In this paper, the authors examine such practices and show how they are typically misguided. The parameters being estimated are no longer well defined, and post-model-selection sampling distributions are mixtures with properties that are very different from what is conventionally assumed. Confidence intervals and statistical tests do not perform as they should. The authors examine in some detail the specific mechanisms responsible. They also offer some suggestions for better practice and show though a criminal justice example using real data how proper statistical inference in principle may be obtained. Figures, tables, and references (Published Abstract)