Using Traffic Stop Data to Investigate Issues of Systemic Racism
In this paper we present an instructional unit that engages students in investigating systemic racism in traffic stops using a dataset from Charlotte, NC and that we have implemented in content and methods classes for pre-service teachers as part of a curriculum development research project. The instructional unit has two main goals: to develop statistical concepts and practices associated with critical statistical literacy, and to normalize discussion of race and racism in STEM classrooms. To help instructors and educational researchers consider how they could translate this unit to their own settings, we provide relevant historical context for race and racism in the U.S. along with pedagogical suggestions for teaching about such issues. We discuss the overall design of the unit and provide examples of student work in relation to the activity, as well as possible extension activities. All materials for the unit including instructors’ guide, pedagogical resources and all activity materials and slides are provided in a permanent online repository for others to use and modify.
Authors
- Travis Weiland (ORCID: https://orcid.org/0000-0002-5901-5683)
- Ksenija Simić-Muller (ORCID: https://orcid.org/0000-0002-0123-5050)
- Anthony Fernandes (ORCID: https://orcid.org/0000-0002-1113-523X)
Institutions
- University of North Carolina at Charlotte (US)
- Pacific Lutheran University (US)
Publication Details
- Journal
- Journal of Statistics and Data Science Education
- Published
- 2026-06-30
- DOI
- https://doi.org/10.1080/26939169.2026.2697728
- Primary Topic
- LGBTQ Health, Identity, and Policy
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- College of Veterinary Medicine, Cornell University