A comprehensive guide to selecting the right modeling strategy for explanatory and predictive data analysis

Declining costs of sequencing technology have catalyzed the widespread use of high-dimensional complex omics datasets in microbiology. While rich in information, these datasets present major analytical challenges, including sparsity, heterogeneity, and the need for robust statistical validation. 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's utility, we analyze a real-world COVID-19 dataset to identify cytokine biomarkers associated with disease severity. By aligning analytical strategies with microbiology inquiry, this guide aims to enhance reproducibility, empower data-informed decisions, and promote more rigorous, interpretable research in microbiology and public health.