SEMINAR – October 25, 2024

Abstract

A significant trend in data analysis over the past 20 years has been the efforts at promoting computational transparency and reproducibility. These efforts have had many benefits, including the wide dissemination of code and datasets that can be used for both verification and extension with new analyses. However, a question remains as to whether computational reproducibility is a useful indicator of the trustworthiness of a data analysis. While reproducible analyses can be checked more easily for problems or errors, a heavy burden is placed on others to similarly reproduce the time and resources to execute the analytic code. We argue that reproducibility, while useful as a minimum standard for trustworthiness, is not sufficient and that other formats for presenting and distributing data analyses should be considered. We borrow ideas from systems engineering and demonstrate some of these techniques through case studies.

About the Speaker

Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas at Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and the Co-Director of the Johns Hopkins Data Science Lab. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. Roger received a PhD in Statistics from the University of California, Los Angeles. His current research focuses on building analytic design theory for improving the quality of data analyses and on the development of statistical methods for addressing environmental health problems.

Event Organizer

Jonathan L. Auerbach
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University

Ben Seiyon Lee
Assistant Professor, Department of Statistics
College of Engineering and Computing
George Mason University