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Abstract Details

Activity Number: 334
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Education
Abstract #324871
Title: P-Value as Strength of Evidence Measured by Confidence Distribution
Author(s): Sifan Liu*
Companies: Rutgers Univ Statistics Dept
Keywords: p-value ; confidence distribution ; hypothesis test ; limiting p-value ; interval hypothesis test

The notion of p-value is fundamental in statistics. However, it is often misinterpreted, misused or miscommunicated. In this article, we propose a general and rigorous definition of p-value that fulfills the two expected characteristics of it. The proposal incorporates all existing definitions of p-value available, and justifies their interpretations. The paper further presents a concrete approach to formulate and calculate p-values based on confidence distribution. It has two main advantages. First, it is applicable for a wide range of hypothesis testing problems, including the conventional one- and two-sided tests, tests with interval-type null, intersection-union tests, multivariate tests and so on. Second, it can naturally lead to a coherent interpretation of p-value as the strength of evidence in support of the null hypothesis, as well as a meaningful measure of degree of such support. Numerical examples are used to illustrate the wide applicability and computational feasibility of our approach. We show that our proposal is a safe and universally effective and can be applied broadly, without further consideration of the form/size of the null space.

Authors who are presenting talks have a * after their name.

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