Non-Parametric Test on Student Performance in Four Different Courses

The academic performance of students is a critical measure of educational success, directly influencing their professional futures and contributing to institutional credibility. This study investigates the variations in student performance across four key courses Sampling Techniques (STA 423), Statistical Theory (STA 426), Medical Statistics (STA 424), and Time Series Analysis (STA 425)—using the Kruskal-Wallis H-test, a robust non-parametric statistical method. Employing a cross-sectional design, data from 169Higher National Diploma (HND) II Statistics students at the Federal Polytechnic, Ede, were analyzed. The research objectives include examining whether significant differences exist in academic performance among the courses and understanding the implications of these differences for curriculum development and pedagogical strategies. The Kruskal-Wallis H-test was chosen because it does not assume normality or homogeneity of variance, making it suitable for analyzing academic scores, which often lack a normal distribution.The findings revealed statistically significant differences in student performance across the courses, highlighting areas requiring curriculum adjustments and additional instructional support. Post-hoc analysis identified specific courses with performance disparities, suggesting targeted interventions to bridge these gaps. The study emphasizes the importance of statistical tools in educational research and underscores the utility of non-parametric methods in analyzing academic outcome.This research contributes to the broader understanding of how course complexity and teaching methodologies impact student performance, providing actionable insights for educators, policymakers, and administrators. It also demonstrates the flexibility and relevance of non-parametric approaches like the Kruskal-Wallis test in addressing real-world research challenges.