ABSTRACT In the analysis of metabolomics data, selecting the appropriate statistical approach is crucial for maximizing model interpretation, predictivity and reliability. This study evaluates the effectiveness of Orthogonal Partial Least Squares (OPLS) models, specifically comparing OPLS‐DA (assuming sample independence) and OPLS‐EP (assuming sample dependency) in datasets of bacterial samples under different experimental conditions. OPLS‐EP consistently demonstrates superior predictive performance, evidenced by higher predictive ability by means of cross‐validation (Q2) compared to OPLS‐DA, indicating greater model significance. Our findings prove the advantages of the paired statistical approach. This approach ensures that treatment effects are accurately measured by minimizing inter‐sample variation and enhancing signal detection. Previous research in metabolomics has demonstrated the benefits of this method for biomarker sensitivity, particularly in matched case–control studies. The present study extends this understanding by applying paired statistical approaches to bacterial isolate treatments, offering novel insights into their utility. Overall, the findings emphasize the importance of OPLS‐EP in enhancing biomarker sensitivity and model reliability in metabolomics research.