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Concerns about the reproducibility of findings across microbiological studies underscore the importance of standardized, transparent analytical approaches. Despite the availability of diverse statistical frameworks and machine learning methods, designing an appropriate statistical workflow (from method selection to model evaluation) remains challenging, particularly for researchers with limited advanced statistical training. Missteps in this process can lead to misinterpretation, irreproducibility, and flawed conclusions. This paper provides a structured, step-by-step framework to guide and validate the methodology of choosing the right statistical methods for both explanatory and predictive modeling in microbiology and translational research. We outline essential decision points spanning data preprocessing, feature selection, model assumptions, and model evaluation, and highlight common trade-offs and practical considerations. To demonstrate the guide\u2019s utility, we analyze a real-world COVID-19 dataset to identify cytokine biomarkers associated with disease severity. 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