Data-dependent construction of discrete-time Markov chain often leads to imprecision in the transition probabilities. A way to incorporate the imprecise probabilities is to represent the probabilities in the form of intervals. Interval discrete-time Markov chains are structures that address the uncertainty in the environment. Querying using temporal logics on uncertain discrete-time Markov chains is computationally intensive. In this work, a tractable formalism is created that leverages probabilistic model checking on an abstraction of a set of discrete-time Markov chains. The output of the temporal logic queries from the set of discrete-time Markov chains is aggregated and analyzed with statistical algorithms by characterization of statistical confidence scores. A case study of the methodology is implemented on a prototype of Galactose regulatory module. The results of the experiments are presented. The results elucidate the methodology and provide information on improvements.