<![CDATA[This study investigates the effectiveness of Simulation-Based Learning (SBL) in improving students’ ability to conduct inferential statistical analysis using SPSS. The research employed a quantitative approach with a sample of 31 students, utilizing observation and testing as data collection methods. Statistical analysis was conducted through normality tests, homogeneity tests, and hypothesis testing. The results of the Kolmogorov-Smirnov and Shapiro-Wilk tests indicated that both pre-test and post-test data were not normally distributed (p < 0.05). Furthermore, Levene’s test revealed that the data did not meet the assumption of homogeneity of variances. Consequently, non-parametric analysis was applied using the Wilcoxon Signed Rank Test. The Wilcoxon test results showed a Z value of -4.794 with a significance level of 0.000 (p < 0.05), confirming a significant difference between pre-test and post-test scores. These findings demonstrate that SBL is effective in enhancing students’ statistical analysis skills, particularly in applying inferential tests through SPSS. Overall, this research provides empirical evidence that SBL is a powerful pedagogical approach for bridging theoretical knowledge and practical application in statistical learning, thereby equipping students with essential competencies for academic and professional contexts. ]]>
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