Objective: Biological stratification is widely used in microbiome research to improve interpretability by reducing heterogeneity, particularly in the small cohorts typical of precision medicine and rare disease studies. Because microbiome composition is strongly influenced by host-specific factors, including genetics, diet, and lifestyle, substantial inter-individual variability is common. Although stratification is frequently employed to address this variability, its consequences for statistical inference under small-sample conditions remain insufficiently quantified.

Materials and Methods: We evaluated the effects of biological stratification on variance, effect-size distributions, and statistical power using a real disease-control microbiome dataset comprising patients with familial Mediterranean fever (FMF) and healthy controls (n = 28). Stratification was performed sequentially by disease status and sex. Variance reduction, effect sizes, minimum detectable effects, and statistical power were assessed across stratification levels.

Results: Stratification produced modest reductions in within-group variability (~3-7%) but substantially reduced effective sample size (n = 4 -10 per subgroup). Consequently, minimum detectable effect sizes increased markedly (d ≈ 1.25-1.98 for 80% power), whereas observed effect sizes remained small (median |d| ≈ 0.2). Although stratification can reveal subgroup-specific patterns that may be obscured in pooled analyses, particularly when biologically distinct groups exhibit opposing trends, the gain in biological resolution was generally insufficient to offset the accompanying loss of statistical power.

Conclusions: These findings demonstrate that modest variance reduction does not compensate for power loss when underlying effect sizes are small. Under such conditions, stratified analyses are more appropriately interpreted within an exploratory framework emphasizing effect-size estimation and variability assessment rather than confirmatory hypothesis testing. Importantly, improved biological resolution does not necessarily translate into improved statistical detectability. More broadly, the results suggest that the adequacy of a given sample size depends on the biological characteristics of the system under investigation. Although derived from an FMF cohort, the findings have broader implications for microbiome study design, subgroup analyses, and the interpretation of microbiome-based interventions.