贝叶斯统计没有什么神奇的:为心理学初学者介绍数据分析中的认知概率

IF 2.5 3区 心理学 Q2 PSYCHOLOGY, SOCIAL
Wojciech Świątkowski, Antonin Carrier
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引用次数: 8

摘要

本文是对贝叶斯推理在心理科学中的应用的一篇读者友好的介绍。我们首先解释频率论和认识论对概率的解释之间的区别,它们分别支撑着频率论和贝叶斯统计。我们用一个具体的例子——一个学生想知道他/她是否携带了恶性统计病毒——来解释这两种方法是如何不同的。在引入数学框架之前,我们用直观的例子来说明贝叶斯推理。讨论了不同的思想流派和建议,以说明如何在贝叶斯因子测试中使用先验。我们讨论了心理学如何从对贝叶斯方法的更大依赖中受益。最后,我们给出了如何用实际数据计算贝叶斯因子分析,并提供了R代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
There is Nothing Magical about Bayesian Statistics: An Introduction to Epistemic Probabilities in Data Analysis for Psychology Starters
Abstract This paper is a reader-friendly introduction to Bayesian inference applied to psychological science. We begin by explaining the difference between frequentist and epistemic interpretations of probability that underpin respectively frequentist and Bayesian statistics. We use a concrete example—a student wondering whether s/he carries the virus statisticus malignum—to explain how both approaches are different one from another. We illustrate Bayesian inference with intuitive examples, before introducing the mathematical framework. Different schools of thoughts and recommendations are discussed to illustrate how to use priors in Bayes Factor testing. We discuss how psychology could benefit from a greater reliance on Bayesian methods. Finally, we illustrate how to compute Bayes Factors analyses with real data and provide the R code.
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来源期刊
CiteScore
4.50
自引率
12.50%
发文量
7
期刊介绍: Basic and Applied Social Psychology (BASP) emphasizes the publication of outstanding research articles, but also considers literature reviews, criticism, and methodological or theoretical statements spanning the entire range of social psychological issues. The journal will publish basic work in areas of social psychology that can be applied to societal problems, as well as direct application of social psychology to such problems. The journal provides a venue for a broad range of specialty areas, including research on legal and political issues, environmental influences on behavior, organizations, aging, medical and health-related outcomes, sexuality, education and learning, the effects of mass media, gender issues, and population problems.
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