Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project

Alexandra Sarafoglou*, Anna van der Heijden, Tim Draws, Joran Cornelisse, Eric Jan Wagenmakers, Maarten Marsman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

48 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)138-150
Number of pages13
JournalPsychology Learning and Teaching
Volume21
Issue number2
DOIs
Publication statusPublished - 2022

Keywords

  • Bayes factor
  • Bayesian inference
  • education
  • encompassing – prior
  • open science
  • replication

Fingerprint

Dive into the research topics of 'Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project'. Together they form a unique fingerprint.

Cite this