Prior exposure: Training in Bayesian data analysis to complement and enhance traditional statistical methods for psychology and the social sciences

贝叶斯方法现在是科学中统计方法的标准体系的一部分，在选举预测、脑成像和临床心理学等领域的心理学和社会科学中正变得越来越重要。然而，尽管这些领域的顶级国际期刊定期使用复杂的贝叶斯方法发表量化研究，但英国心理学家和社会心理学家获得贝叶斯数据分析技能的机会很少。英国的社会科学研究生培训通常不会引入这些方法，而传统统计方法的本科生和研究生培训也不会涵盖贝叶斯方法高级培训所需的一些基本哲学和统计学概念。因此，为心理学家和社会科学家提供贝叶斯数据分析培训涉及克服许多障碍。这些差异包括先前培训的差异(例如，那些具有基本的定量社会科学基础的人，以及那些在非贝叶斯背景下具有更先进方法经验的人)和技术能力方面的差异(从熟悉电子表格和基本统计包的学生到具有编写自己的数据分析计算机代码的经验的学生)。然而，最大的障碍可能是贝叶斯统计方法与传统统计方法在理念上的差异。尽管不同的哲学在历史上一直是冲突的根源，但统计科学的最新发展强调了利用这两套方法的实际优势。我们提出了一个培训包，旨在将贝叶斯工具作为补充和增强传统统计方法的一种方式。我们的培训包包括四个为期一天的系列研讨会，从介绍贝叶斯数据分析的原理和实践开始，以复杂的贝叶斯工具结束，这些工具允许研究人员处理难以使用传统方法建模的混乱的现实世界数据。参与者可以参加一个或多个单独的研讨会(例如，为了足够了解基本的贝叶斯方法来审查一篇论文或改善他们与更专业的同事的合作)，或者参加整个序列。除讲习班外，最后一次专题讨论会将为研究学生和早期职业研究人员提供机会，介绍涉及贝叶斯数据分析的工作以及该领域专家的论文。研讨会的首要主题是贝叶斯数据分析的教学。项目完成后，将根据开放源码许可证向研究人员提供培训资源，以促进开发新的培训，并支持将贝叶斯数据分析纳入课程。

Bayesian methods are now part of the standard repertoire of statistical methods in science and are becoming increasingly important in psychology and the social sciences in field as diverse as election forecasting, brain imaging and clinical psychology. However, while top international journals in these fields regularly publish quantitative work using sophisticated Bayesian methods, opportunities for psychologists and social psychologists in the UK to acquire skills in Bayesian data analysis are few and far between. Postgraduate social science training in the UK does not typically introduce these methods, while undergraduate and postgraduate training in traditional statistical methods will not cover some fundamental philosophical and statistical concepts necessary for advanced training in Bayesian methods.Offering training in Bayesian data analysis for psychologists and social scientists therefore involves overcoming a number of barriers. These include the differences in prior training (e.g., those with a basic quantitative social science grounding and those with experience of more advanced methods in a non-Bayesian context) and differences in technical competence (ranging from students familiar with spreadsheets and basic statistical packages to those with experience writing their own computer code for data analysis). However, perhaps the largest barrier is the difference in philosophy between Bayesian and traditional statistical methods. Although the different philosophies have historically been a source of conflict, recent developments in statistical science emphasise the practical advantages of drawing on both sets of methods. We propose a training package that aims to present Bayesian tools as a way to complement and enhance traditional statistical methods.Our training package comprises a series of four one-day workshops that begin with an introduction to the philosophy and practice of Bayesian data analysis and end with sophisticated Bayesian tools that allow researchers to handle messy real world data that are difficult to model using traditional approaches. Participants may attend one or more individual workshops (e.g., to understand basic Bayesian methods enough to review a paper or improve their collaboration with a more expert colleague) or attend the entire sequence. In addition to the workshops a final symposium will offer an opportunity for research students and early career researchers to present work involving Bayesian data analysis alongside papers by experts in the field. The overarching theme for the symposium is the teaching of Bayesian data analysis. On completion of the project the training resources will be made available to researchers under an open source license in order to facilitate the development of new training and to support the integration of Bayesian data analysis into the curriculum.