You are here: Vanderbilt Biostatistics Wiki>Main Web>Seminars>MatthewShotwellAug282013 (26 Aug 2013, AudreyCarvajal)EditAttach

The most important products of applied statistical work are statistical inferences, e.g., hypothesis tests and confidence regions. Hence, the statistician is primarily concerned with assuring that statistical inferences are correct, with regard to their degree of uncertainty, e.g., type I error or coverage probability. By convention, asymptotic arguments, and notably the central limit theorem, are most commonly used to bolster the assumed correctness of statistical inferences. This presentation examines several examples where this paradigm falters, and why it may be wholly unnecessary. Potential empirical alternatives are presented to directly verify the correctness of statistical inferences, given assumptions that are usually no stronger than those associated with asymptotic results. -- AudreyCarvajal - 09 Aug 2013

Edit | Attach | Print version | History: r2 < r1 | Backlinks | View wiki text | Edit wiki text | More topic actions

Topic revision: r2 - 26 Aug 2013, AudreyCarvajal

Copyright © 2013-2022 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.

Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback

Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback