Beyond sample splitting: valid inference while double-dipping

Seminar Series

Friday, November 20, 2020 - 12:00
Daniela Witten, PhD

Abstract:  As datasets continue to grow in size, in many settings the focus of data collection has shifted away from testing pre-specified hypotheses, and towards hypothesis generation. Researchers are often interested in performing an exploratory data analysis in order to generate hypotheses, and then testing those hypotheses on the same data; I will refer to this as 'double dipping'. Unfortunately, double dipping can lead to highly-inflated Type 1 errors. In this talk, I will consider the special case of hierarchical clustering. First, I will show that sample-splitting does not solve the 'double dipping' problem for clustering. Then, I will propose a test for a difference in means between estimated clusters that accounts for the cluster estimation process, using a selective inference framework. I will also show an application of this approach to single-cell RNA-sequencing data. This is joint work with Lucy Gao (University of Waterloo) and Jacob Bien (University of Southern California).

Speaker: Daniela Whitten, PhD
Professor of Biostatistics and Statistics
Dorothy Gilford Endowed Chair of Mathematical Statistics,
University of Washington