How to Handle Multiple Statistical Comparisons (Virtual)

Multiple comparisons is a common problem in statistics. If you don't deal with it correctly, you can inflate the false positive rate. You don't want that! This workshop introduces a range of techniques for making adjustments to significance levels when performing multiple analyses of your data. More importantly, you'll learn ways to avoid having to do this.

Prerequisites: Participants should be familiar with introductory-level statistics, including the concepts of significance tests, confidence intervals, and p-values.

This workshop complements our other data science services and support for researchers. All Northwestern researchers (student, staff, and faculty in all fields) can request free consultations with our Data Scientists and Statisticians to guide you through your data project, from considering the feasibility and choosing a method to troubleshooting the code and suggesting improvements. If you're looking for more extensive support, we partner on faculty-sponsored research through our project collaboration service.