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The radiological characterization of contaminated elements (walls, grounds,objects) from nuclear facilities often suffers from a too small number ofmeasurements. In order to determine risk prediction bounds on the level ofcontamination, some classic statistical methods may then reveal unsuited asthey rely upon strong assumptions (e.g. that the underlying distribution isGaussian) which cannot be checked. Considering that a set of measurements ortheir average value arise from a Gaussian distribution can sometimes lead toerroneous conclusion, possibly underconservative. This paper presents severalalternative statistical approaches which are based on much weaker hypothesesthan Gaussianity. They result from general probabilistic inequalities andorder-statistics based formula. Given a data sample, these inequalities make itpossible to derive prediction intervals for a random variable, which can bedirectly interpreted as probabilistic risk bounds. For the sake of validation,they are first applied to synthetic data samples generated from several knowntheoretical distributions. In a second time, the proposed methods are appliedto two data sets obtained from real radiological contamination measurements.

Probabilistic risk bounds for the characterization of radiological contamination