[en] Protection of the environment from anthropogenic radiation is an on-going international concern. The paradigm currently in use argues that the population is adequately protected if the maximally exposed individual's dose is below a certain limit. Based on data sampled from natural populations, resource managers need to be able to test the hypothesis that the maximally exposed individual's dose is acceptable. Recognizing the difficulty of sampling the maximally exposed individual within a contaminated environment, risk assessors have used various alternative approaches. One statistic currently used is the upper 95% confidence limit on the sample mean. An alternative approach is to make no distributional assumptions and use the sample maximum value as an estimate of the maximally exposed individual. Other managers assume that an increased conservatism, added to the model parameters used to estimate risk, will compensate for the inability to sample the maximally exposed individual. While some risk assessors have changed the internationally accepted paradigm and applied recommended dose limits to representatively, rather than the maximally exposed individuals. We propose an alternative: given a sample, find the maximum likelihood estimates of the assumed population parameters and use the 99^th percentile as an estimate of the maximally exposed individual. To determine the effectiveness of our proposed alternative, we use computer simulation techniques to generate a 'population' of doses with known distributional qualities, and then mathematically 'sample' this population and compare the different statistics. The simulation procedure is repeated many times, each time producing a measure of the distance between the estimate and the 'true' value. We are thus able to quantify the bias associated with several approaches used to determine compliance with dose limits. (author)