The radiological characterization of contaminated elements (walls, grounds, objects) from nuclear facilities often suffers from too few measurements. In order to determine risk prediction bounds on the level of contamination, some classic statistical methods may therefore be unsuitable, as they rely upon strong assumptions (e.g., that the underlying distribution is Gaussian) which cannot be verified. Considering that a set of measurements or their average value come from a Gaussian distribution can sometimes lead to erroneous conclusions, possibly not sufficiently conservative. This paper presents several alternative statistical approaches which are based on much weaker hypotheses than the Gaussian one, which result from general probabilistic inequalities. Given a data sample, these inequalities make it possible to derive prediction intervals for a random variable which can be directly interpreted as probabilistic risk bounds. The proposed methods are applied to several data sets obtained from real radiological contamination measurements to provide more realistic risk bound estimation.