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Freedman's paradox
In statistical analysis, Freedman's paradox,〔Freedman, D. A. (1983) "A note on screening regression equations." ''The American Statistician'', 37, 152–155.〕 named after David Freedman, describes a problem in model selection whereby predictor variables with no explanatory power can appear artificially important. Freedman demonstrated (through simulation and asymptotic calculation) that this is a common occurrence when the number of variables is similar to the number of data points. Recently, new information-theoretic estimators have been developed in an attempt to reduce this problem,〔Lukacs, P. M., Burnham, K. P. & Anderson, D. R. (2010) "Model selection bias and Freedman's paradox." ''Annals of the Institute of Statistical Mathematics'', 62(1), 117–125 〕 in addition to the accompanying issue of model selection bias,〔Burnham, K. P., & Anderson, D. R. (2002). ''Model Selection and Multimodel Inference: A Practical-Theoretic Approach,'' 2nd ed. Springer-Verlag.〕 whereby estimators of predictor variables that have a weak relationship with the response variable are biased.
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