Monitoring recreational waters for fecal contamination is an important responsibility of water resource management agencies throughout the world, yet fecal indicator bacteria (FIB)-based recreational water quality assessments rarely distinguish between analytical, spatial, and temporal variability. To address this gap in water resources research and management protocol, we compare two methods for quantifying FIB concentration variability at a frequently-used beach on Lake Huron (Michigan, USA). The first method calculates differences between most probable number (MPN) and colony-forming unit (CFU) values derived from conventional analysis procedures. The second method uses the “raw data” from these analysis procedures in a Bayesian hierarchical model to explicitly acknowledge analytical variability and subsequently infer the relative significance of the effect of sampling location and time on in situ FIB concentrations. Results of the Bayesian analysis indicate that in situ FIB concentrations do not vary significantly over small spatial and temporal scales, and that observed differences in MPN and CFU values over these same spatial and temporal scales are due almost entirely to intrinsic variability introduced by laboratory analysis procedures. Our findings underscore potential opportunities for incorporating Bayesian statistical models directly into routine recreational water quality assessments and for advancing the state of the art in methods for protecting humans from waterborne disease.