The widespread occurrence and persistence of the per-and polyfluoroalkyl substances (PFAS), in the environment may pose significant health risks to humans. Fish consumption is a direct pathway for exposure prompting extensive monitoring. In the United States, fish consumption advisories for PFAS generally focus on perfluorooctanesulfonate (PFOS), the most frequent and abundant PFAS compound reported in fish. However, PFAS analysis is costly, leading monitoring programs to frequently use composite samples —combining different fish of the same species. This financial saving comes at an analytical cost of lost information on individuals’ variation. It is unclear how this may influence threshold-based decisions. We used a modelling approach using aggregated data distribution from individual freshwater fish. We simulated the measurement of PFOS for individual fish compared to composites (3, 5, and 10 fish) at different levels of error structure. We assumed a composite is the mean of the components. These values were then compared to a typical do-not-eat threshold (200 ng•g wet mass-1). These results show that the probability of assigning PFOS levels above or below the threshold are shifted with a composite sample by masking individual value contributions. Because true individual contaminant loads are variable, composite sampling is a more conservative metric for contaminant thresholds. The greater the variability, the lower the detection probability for truly contaminated samples. Our study may help to inform sampling for monitoring programs aimed at managing human exposure to PFAS.