Statistical testing frameworks for process efficiency and variability management

This study explores the comparative efficacy of parametric and nonparametric statistical tests in analyzing clinical metrics, specifically weight (Peso (Kg)), height (H), and Body Mass Index (BMI (kg/m²)) for Glucagon-like Peptide-۱ (GLP۱) and Sodium-Glucose Co-Transporter-۲ (SGLT۲) treatment groups. By applying a range of statistical methods, including the Sign Test, Wilcoxon Signed-Ranks Test, Wilcoxon Rank-Sum Test, Kruskal-Wallis Test, and Chi-Square Test for nonparametric analysis, alongside parametric tests such as the Paired t-test, Independent Samples t-test, and Analysis of Variance (ANOVA), we assess the sensitivity and reliability of these approaches under varying data conditions. Python software was utilized for executing these statistical analyses, ensuring precise and reproducible results. The findings indicate that while nonparametric tests offer robustness against assumptions of normality and are effective for small sample sizes and skewed distributions, parametric tests display greater sensitivity, particularly in detecting differences in weight. Notably, significant differences were observed in BMI across both test types, underscoring its variability between treatments. The comprehensive analysis highlights the importance of selecting appropriate statistical methods based on data characteristics to ensure accurate and reliable findings in clinical research.

کلیدواژه ها:

Parametric Statistical Tests ، Nonparametric Statistical Tests ، Clinical Data Analysis ، Python Software ، Body mass index