Efficient alternatives for Bayesian hypothesis tests in psychology.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Psychological methods Pub Date : 2024-04-01 Epub Date: 2022-04-14 DOI:10.1037/met0000482
Sandipan Pramanik, Valen E Johnson
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引用次数: 0

Abstract

Bayesian hypothesis testing procedures have gained increased acceptance in recent years. A key advantage that Bayesian tests have over classical testing procedures is their potential to quantify information in support of true null hypotheses. Ironically, default implementations of Bayesian tests prevent the accumulation of strong evidence in favor of true null hypotheses because associated default alternative hypotheses assign a high probability to data that are most consistent with a null effect. We propose the use of "nonlocal" alternative hypotheses to resolve this paradox. The resulting class of Bayesian hypothesis tests permits more rapid accumulation of evidence in favor of both true null hypotheses and alternative hypotheses that are compatible with standardized effect sizes of most interest in psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract Image

心理学中贝叶斯假设检验的有效替代方案。
近年来,贝叶斯假设检验程序得到了越来越多的认可。与经典测试程序相比,贝叶斯测试的一个关键优势是它们有可能量化支持真零假设的信息。具有讽刺意味的是,贝叶斯测试的默认实现阻止了有利于真正零假设的有力证据的积累,因为相关的默认替代假设为最符合零效应的数据分配了高概率。我们建议使用“非局部”替代假设来解决这个悖论。由此产生的一类贝叶斯假设测试允许更快速地积累有利于真零假设和替代假设的证据,这些假设与心理学中最感兴趣的标准化效应大小相兼容。(PsycInfo数据库记录(c)2022 APA,保留所有权利)。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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