SummarySingle-cell human disease studies are able to identify differentially abundant cell states between conditions such as disease versus healthy, but require precious clinical samples and costly technologies. Therefore, it is critical to employ study design principles that maximize power to detect differential abundance. Here, we present single-cell POwer Simulation Tool (scPOST), a method that enables users to estimate power under different study designs. To approximate the specific experimental and clinical scenarios being investigated, scPOST takes prototype (public or pilot) single-cell data as input and generates large numbers of single-cell datasets in silico. We use scPOST to perform power analyses with three independent single-cell datasets that span diverse experimental conditions and tissues. Over thousands of simulations, we consistently observe that power to detect differential abundance is maximized by larger numbers of independent samples, reduced batch effects, and lower cell state frequency variation across samples.