In this paper, the novel technique of intelligent constructing adequate statistical decision rules under parametric uncertainty of applied mathematical models via the smart use of pivotal quantities and ancillary statistics is proposed. It is assumed that only the functional form of the underlying distributions is specified, but some or all of its parameters are unspecified. In such cases pivotal quantities and ancillary statistics, whose distribution does not depend on the unknown parameters, are used. Eliminating unknown (nuisance) parameters from a model is universally recognized as a major problem of statistics. The classical method of elimination of unknown (nuisance) parameters from the model, which is used repeatedly in the large sample theory of statistics, is to replace the unknown (nuisance) parameter by an estimated value. However, this method is not efficient when dealing with small data samples. The novel statistical technique of computational intelligence isolates and eliminates unknown parameters from the underlying model as efficiently as possible. Unlike the Bayesian approach, which is dependent of the choice of priors, the proposed method is independent of the choice of priors and represents a novelty in the theory of statistical decisions. It allows one to eliminate unknown parameters from the problem and to find the efficient statistical decision rules, which often have smaller risk than any of the well-known decision rules. It is conceptually simple and easy to use. To illustrate the proposed technique, numerical examples are given.