Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we incorporated such a holistic structure in a Bayesian research project on ordered binomial probabilities. The project was conducted with a group of three undergraduate psychology students who had basic knowledge of Bayesian statistics and programming, but lacked formal mathematical training. The research project aimed to (1) convey the basic mathematical concepts of Bayesian inference; (2) have students experience the entire empirical cycle including collection, analysis, and interpretation of data and (3) teach students open science practices.

统计界的当前发展表明，现代统计教育应该从整体上构建，即允许学生使用真实数据并回答具体的统计问题，同时也通过对他们进行有关替代框架的教育，例如贝叶斯推理。在本文中，我们描述了我们如何将这种整体结构纳入关于有序二项式概率的贝叶斯研究项目中。该项目由三名心理学本科生进行，他们具有贝叶斯统计和编程的基本知识，但缺乏正规的数学训练。该研究项目旨在 (1) 传达贝叶斯推理的基本数学概念；(2) 让学生体验整个经验周期，包括收集、分析、