This paper deals with the problem of estimating the probability under which a model set is not falsified by a set of measured plant frequency response samples. A definition of sample unfalsified probability has been proposed, and an explicit formula has been derived. Computation issues are also discussed. Moreover, an efficient algorithm has been developed for sample unfalsified probability calculation. Monte Carlo simulations show that the defined sample unfalsified probability is appropriate in the evaluation of the quality of a model set. Compared with the deterministic approach, simulation results suggest that the probabilistic approach is more suitable in model set validation.