Department of Psychological Sciences, University of California, Merced.
Department of Methods and Statistics, Utrecht University.
Psychol Methods. 2017 Jun;22(2):240-261. doi: 10.1037/met0000065. Epub 2015 Dec 21.
Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these 3 points of potential danger, we have developed a succinct checklist: the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis. We provide an account of "when to worry" for each of these issues related to: (a) issues to check before estimating the model, (b) issues to check after estimating the model but before interpreting results, (c) understanding the influence of priors, and (d) actions to take after interpreting results. To accompany these key points of concern, we will present diagnostic tools that can be used in conjunction with the development and assessment of a Bayesian model. We also include examples of how to interpret results when "problems" in estimation arise, as well as syntax and instructions for implementation. Our aim is to stress the importance of openness and transparency of all aspects of Bayesian estimation, and it is our hope that the WAMBS questionnaire can aid in this process. (PsycINFO Database Record
贝叶斯统计方法正在缓慢地渗透到各个科学领域，并在应用研究中越来越受欢迎。虽然使用贝叶斯统计非常有吸引力，但我们的个人经验使我们相信，由于至少 3 个主要原因，盲目地应用贝叶斯方法可能是危险的：先验的潜在影响、对贝叶斯特征和结果的误解以及贝叶斯结果的不当报告。为了解决这 3 个潜在的危险点，我们开发了一个简洁的清单：WAMBS 清单（何时担心以及如何避免误用贝叶斯统计）。该问卷的目的是描述在应用贝叶斯分析时需要彻底检查的 10 个要点。我们提供了与以下相关的每个问题的“何时担心”的说明：（a）在估计模型之前要检查的问题，（b）在估计模型后但在解释结果之前要检查的问题，（c）理解先验的影响，以及（d）在解释结果后要采取的行动。为了配合这些关注要点，我们将提供可以与贝叶斯模型的开发和评估一起使用的诊断工具。我们还包括了当估计出现“问题”时如何解释结果的示例，以及语法和实施说明。我们的目的是强调贝叶斯估计的所有方面的开放性和透明度的重要性，我们希望 WAMBS 问卷能够在这一过程中提供帮助。
