Exercise and sport sciences increasingly rely on statistical models to analyse complex data arising from experimental, observational, and longitudinal designs. However, applied research frequently shows misalignment between research questions, data structure, and analytical strategies, leading to biased inference, limited reproducibility, and reduced interpretability. Common challenges include small sample sizes, clustered and repeated measurements, suboptimal model selection, insufficient assessment of model assumptions, and the growing use of machine learning methods without transparent validation or reporting. This methodological narrative review synthesises key statistical challenges through a targeted literature search and proposes a decision-oriented framework to guide statistical model selection and reporting in exercise and sport sciences. The framework integrates study objective (explanatory vs. predictive), outcome type, data structure, and information content to support coherent and transparent analytical decisions. Classical models, penalised regression, and supervised machine learning approaches are discussed in terms of applicability, assumptions, interpretability, and validation requirements. A workflow figure and practical decision tables are provided to support model selection, test selection, validation, and robustness assessment. The overarching goal is to strengthen applied statistical reasoning and promote robust, interpretable, and reproducible quantitative research in exercise and sport sciences.