Seminar by Jonathan Huggins

Reproducible Statistical Inference

If slightly changing a model specification or including more data results in contradictory inferences, then the validity of any conclusions drawn from such inferences is put in doubt: they are not, in a statistical sense, reproducible. Motivated by examples ranging from phylogenetic tree reconstruction to cell type identification, I’ll discuss three ways standard likelihood-based inference methods can produce non-reproducible results. I will describe the source of these problems – all of which implicate model misspecification – and propose some easy-to-implement solutions to fix them.