A Comparison of Reproducibility Guidelines and Its Implications on Undergraduate Statistical Education

Abstract

In this paper, we replicated a Bayesian educational research project, which explores the association between broadband access and online course enrollment in the US. We summarized key findings from our replication and compared them with the original project. Based on my replication experience, we aim to demonstrate the challenges of research reproduction, even when codes and data are shared openly and the quality of the materials on GitHub are high. Moreover, we investigate the implicit presumptions of the researchers' level of knowledge and discuss how such presumptions may add difficulty to the reproduction of scientific research. Finally, we hope this article sheds light on the design of reproducibility criterion and opens up a space to explore what should be taught in undergraduate statistics education.