Factors Associated With Unit‐Specific Failure in a University‐Level Statistics Course

Failure in introductory statistics courses is commonly evaluated using overall course grades, potentially obscuring important differences across content areas. This study examines factors associated with failure across four instructional units of a General Statistics course taught at a private university in Colombia: Exploratory Data Analysis, Probability and Random Variables, Statistical Inference, and Linear Regression. Using a complete‐case analytical sample of 186 undergraduate students, we estimated unit‐specific logistic regression models combining exam performance and pre‐exam self‐perception measures. Failure rates varied substantially across units, ranging from 19% to 46%. Students from programs outside the School of Applied Sciences and Engineering consistently exhibited higher odds of failure, while academic stage, prior programming experience, and self‐perceived competence showed unit‐specific effects. Robustness analyses supported the stability of these findings. The results demonstrate that academic vulnerability in statistics varies across content areas and that unit‐level analyses reveal patterns hidden by aggregate course outcomes, informing more targeted pedagogical interventions.