Observing many students using difference-in-differences designs on the same data and hypothesis reveals a universe of uncertainty

The rise of many-analysts studies has highlighted substantial variability inresearch outcomes when different teams independently analyze the same datasetsand hypotheses. This paper makes two key contributions to this emerging field. First, we demonstrate how the many-analysts framework can be integrated into research methods education, by having students act as independent analysts addressing the same research question, followed by a meta-analysis of their results. Enriching the pedagogical toolkit, this didactic approach teaches concepts like researcher degrees of freedom and the importance epistemic humility. Second, this study applies the many-analysts framework to causal inference using observational data to identify outcome variability in this area of research. Specifically, we engage students in independent difference-in-differences (DiD) analyses focused on local governance. The study reveals a wide range of effect sizes and divergent conclusions from a typical DiD design, emphasizing the benefits of robustness checks involving multiple independent analysts in research and teaching.