2018/10/17 by Alexandra Sarafoglou, Sarafoglou, Alexandra, Anna van der Heijden +10
Mathematics · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Innovations in Educational Methods #Statistics Education and Methodologies #stat.AP
paper · pdf · doi:10.48550/arxiv.1810.07496
openalex publication_date 2018/10/17 · arxiv created 2022/01/06 · arxiv updated 2022/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.