心理科学进展2018，Vol.26Issue(6)：951-965,15.DOI:10.3724/SP.J.1042.2018.00951
贝叶斯因子及其在JASP中的实现
The Bayes factor and its implementation in JASP: A practical primer
胡传鹏 1孔祥祯 2Eric-Jan Wagenmakers 3Alexander Ly 4彭凯平4
作者信息
- 1. 清华大学心理学系, 北京 100084
- 2. Neuroimaging Center, Johannes Gutenberg University Medical Center, 55131 Mainz, Germany
- 3. Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
- 4. Department of Psychological Methods, University of Amsterdam, 1018 VZ Amsterdam, The Netherlands
- 折叠
摘要
Abstract
Statistical inference plays a critical role in modern scientific research, however, the dominant method for statistical inference in science, null hypothesis significance testing (NHST), is often misunderstood and misused, which leads to unreproducible findings. To address this issue, researchers propose to adopt the Bayes factor as an alternative to NHST. The Bayes factor is a principled Bayesian tool for model selection and hypothesis testing, and can be interpreted as the strength for both the null hypothesis H0and the alternative hypothesisH1based on the current data. Compared to NHST, the Bayes factor has the following advantages: it quantifies the evidence that the data provide for both the H0and the H1, it is not"violently biased" against H0, it allows one to monitor the evidence as the data accumulate, and it does not depend on sampling plans. Importantly, the recently developed open software JASP makes the calculation of Bayes factor accessible for most researchers in psychology, as we demonstrated for the t-test. Given these advantages, adopting the Bayes factor will improve psychological researchers' statistical inferences. Nevertheless, to make the analysis more reproducible, researchers should keep their data analysis transparent and open.关键词
贝叶斯因子/贝叶斯学派/频率学派/假设检验/JASPKey words
Bayes factor/Bayesian statistics/Frequentist/NHST/JASP分类
社会科学引用本文复制引用
胡传鹏,孔祥祯,Eric-Jan Wagenmakers,Alexander Ly,彭凯平..贝叶斯因子及其在JASP中的实现[J].心理科学进展,2018,26(6):951-965,15.
