Freedman's Paradox

Freedman's Paradox

Release Date:  //1983
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Medium:  Paradox
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Release Message:  It describes a problem in model selection where predictor variables with no explanatory power can appear artificially important. Authored by David A. Freedman.
Description:  In statistical analysis, Freedman's paradox, 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, in addition to the accompanying issue of model selection bias, whereby estimators of predictor variables that have a weak relationship with the response variable are biased.