Combine Statistical Thinking With Scientific Practice: A Protocol of a Bayesian Thesis Project For Undergraduate Students
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Current developments in the statistics community suggest that modern statistics education should be structured holistically, i.e., by allowing students to work with real data and answer concrete statistical questions, but also by educating them about alternative statistical frameworks, such as Bayesian statistics. In this article, we describe how we incorporated such a holistic structure in a Bayesian thesis project on ordered binomial probabilities. The project was targeted at undergraduate students in psychology with basic knowledge in Bayesian statistics and programming, but no formal mathematical training. The thesis project aimed to (1) convey the basic mathematical concepts of Bayesian inference, (2) let students experience the entire empirical cycle including the collection, analysis, and interpretation of data, and (3) teach students open science practices.
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