The development of a probabilistic model for validating Gange's learning hierarchies is described. Learning hierarchies are defined as paired networks of intellectual tasks arranged so that a substantial amount of positive transfer occurs from tasks in a lower position to connected ones in a higher position. This probabilistic validation technique is compared to two other selected validation techniques on 32 experimental populations representing valid and invalid hierarchies, various probabilities of measurement error, and several levels of skill difficulty. From each of the populations described, 1,000 random samples of four different sizes were generated by computer to obtain estimates of the probability of making an incorrect decision about the validity of the hierarchy for each technique. Results support the hypothesis that the developed technique is more accurate than the comparison techniques over all population-sample size combinations studied. (Author/CP)