Hierarchical Bayesian modeling is a powerful tool for analyzing traffic safety studies. It combines prior knowledge with data to create accurate models without needing calibrated factors. This method requires less data and allows for flexible analysis of crash types and variables. By using Bayesian methodology, Departments of Transportation can benefit greatly from more efficient and effective research. The approach discussed in the article helps bridge the gap between basic understanding and practical application of Bayesian methods in traffic studies. The study evaluates the effectiveness of cable barriers on Utah highways, demonstrating the versatility and usefulness of hierarchical Bayesian modeling in various research scenarios.