The Bayes factor and its implementation in JASP: A practical primer

doi:10.3724/SP.J.1042.2018.00951

Abstract

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.

Key words: Bayes factor, Bayesian statistics, Frequentist, NHST, JASP

CLC Number:

B841

HU Chuan-Peng, KONG Xiang-Zhen, Eric-Jan WAGENMAKERS, Alexander LY, PENG Kaiping. The Bayes factor and its implementation in JASP: A practical primer[J]. Advances in Psychological Science, 2018, 26(6): 951-965.

Figures/Tables 6

References 98

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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H₀和H₁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

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H₀和H₁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

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