Generalized p-value: Difference between revisions

Revision as of 21:47, 8 December 2009

Revision as of 21:48, 8 December 2009

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In [[statistics]], '''generalized p-values''' are used to give exact solutions of problems for which the classical method of [[p-value]]s gives only approximate solutions, the approximations being good only when the [[sample size]]s are big enough.

Conventional statistical methods do not provide exact solutions to many statistical problems, especially when the problem involves many [[nuisance parametersparameter]]s. As a result, practitioners often resort to approximate statistical methods or asymptotic statistical methods that are primarily based on large samples. With small samples, in most cases, these approximate methods and asymptotic methods perform very poorly. Due to the utilization of these approximate and asymptotic methods, experimenters often fail to detect the significance of their experiments. Furthermore, there are well-documented cases where these methods not only fail to detect the [[statistical significance]], but may also lead to misleading conclusions.

Generalized p-values method is an exact statistical method based on exact probability statements rather than asymptotic statistical methods to tackle difficult statistical problems where conventional statistical methods do not provide exact solutions. Moreover, the generalized p-value approach is an extension of the classical p-value approach.