Building Data Analysis Proofs

The lecture describes how data analyses are often performed as a sequence of imperative commands, the publication of which is crucial for reproducibility. However, code and results alone are insufficient to make the analyst's underlying assumptions and reasoning comprehensible.

Therefore, a formal representation is proposed that explicitly depicts the logical structure of an analysis. This representation is intended to help evaluate analyses even without access to the data, to reveal logical relationships, and to test the sensitivity of assumptions. Finally, the presentation announces a concrete implementation of this method and its application to typical data analysis tasks.

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.