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Advances in Psychological Science    2018, Vol. 26 Issue (6) : 951-965     DOI: 10.3724/SP.J.1042.2018.00951
Research Method |
The Bayes factor and its implementation in JASP: A practical primer
Chuan-Peng HU1,2(),Xiang-Zhen KONG3,WAGENMAKERS Eric-Jan4,LY Alexander4,5,Kaiping PENG1()
1 Department of Psychology, School of Social Science, Tsinghua University, Beijing 100084, China
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
5 Centrum Wiskunde & Informatica, 1090 GB Amsterdam, The Netherlands
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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 H0 and the alternative hypothesis H1 based 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 H0 and 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.

Keywords Bayes factor      Bayesian statistics      Frequentist      NHST      JASP     
ZTFLH:  B841  
Corresponding Authors: Chuan-Peng HU,Kaiping PENG     E-mail: hcp4715@hotmail.com;pengkp@mail.tsinghua.edu.cn
Issue Date: 28 April 2018
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Chuan-Peng HU
Xiang-Zhen KONG
WAGENMAKERS Eric-Jan
LY Alexander
Kaiping PENG
Cite this article:   
Chuan-Peng HU,Xiang-Zhen KONG,WAGENMAKERS Eric-Jan, et al. The Bayes factor and its implementation in JASP: A practical primer[J]. Advances in Psychological Science, 2018, 26(6): 951-965.
URL:  
http://journal.psych.ac.cn/xlkxjz/EN/10.3724/SP.J.1042.2018.00951     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2018/V26/I6/951
假设检验中的问题 贝叶斯因子 传统推理 参考文献
1. 同时考虑H0H1的支持证据 × 10, 11
2. 可以用来支持H0 × 12, 13
3. 不“严重”地倾向于反对H0 × 14, 15, 16
4. 可以随着数据累积来监控证据的强度 × 17, 18
5. 不依赖于未知的或者不存在的抽样计划 × 19, 20
  
贝叶斯因子, BF10 解释
> 100 极强的证据支持H1
30 ~ 100 非常强的证据支持H1
10 ~ 30 较强的证据支持H1
3 ~ 10 中等程度的证据支持H1
1 ~ 3 较弱的证据支持H1
1 没有证据
1/3 ~ 1 较弱的证据支持H0
1/10 ~ 1/3 中等程度的证据支持H0
1/30 ~ 1/10 较强的证据支持H0
1/100 ~ 1/30 非常强的证据支持H0
< 1/100 极强的证据支持H0
  
  
  
  
  
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[1] WANG Meng-Cheng, DENG Qiaowen, BI Xiangyang.  Latent variable modeling using Bayesian methods[J]. Advances in Psychological Science, 2017, 25(10): 1682-1695.
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