Sonja Winter, an Assistant Professor at the University of Missouri specializing in Bayesian methods for educational research, dives into intriguing discussions. She elaborates on the importance of prior sensitivity analysis for robust findings and how Bayesian techniques elegantly address missing data issues. The conversation also tackles the challenges of overfitting in structural equation modeling, emphasizing the need for caution in model selection. Winter's insights highlight the transformative potential of Bayesian approaches in navigating complex educational data.