Adequate sample sizes are crucial in human microbiome studies for detecting biologically meaningful signals. However, estimating the required power for these studies is challenging due to the high dimensionality, sparsity, and compositional nature of microbiome data. To address this, we developed a simulation-based framework that evaluates how different study designs affect the recovery of differential microbial taxa. We resampled publicly available, deeply sequenced gut metagenomic profiles from Metalog using SIMBA, introducing effects into a user-defined subset of taxa. Our simulations varied in cohort size, effect strength, proportion of affected taxa, and case-to-control balance. We assessed differential abundance using the Wilcoxon rank-sum test and compared the detected taxa with the implanted ground truth. We calculated recall, precision, observed false discovery rate, F1 score, and Matthews correlation coefficient across all scenarios. Preliminary applications across hundreds of simulations showed that increasing sample size improved the recovery of implanted taxa. However, performance was significantly influenced by effect strength, the proportion of affected taxa, and class imbalance. Smaller effects and greater class imbalance required larger sample sizes. These findings suggest that a single universal value cannot adequately represent sample size requirements. The resulting performance curves and heatmaps directly link design parameters to expected biomarker recovery performance. This framework offers a microbiome-specific approach to power analysis based on realistic data. It may facilitate more reproducible study designs and can be extended to machine-learning classification and biomarker discovery.