ABSTRACT
Traditional teaching often emphasizes correct methods, limiting opportunities to explore analytical errors and biases. We introduce a seminar framework that integrates peer-to-peer teaching with intentional exposure to statistical and machine learning mishaps through flawed, student-designed case studies. Students delivered two presentations: one teaching a chosen mishap and another presenting an original case study embedding errors. Personalized feedback before and after each presentation supported iterative improvement. This structure created a safe environment to intentionally produce, analyze, and discuss errors, helping students understand how mishaps arise, affect results, and can be addressed. Class discussions and peer analysis encouraged deeper engagement and critical thinking. A follow-up questionnaire administered 1 year later showed that seminar participants significantly outperformed peers in identifying and correcting data analysis errors. Our results suggest that peer-led, error-focused learning with repeated feedback enhances engagement, retention, and preparedness for real-world data challenges, offering a valuable complement to traditional, instructor-led teaching.