In this paper, a new and simple approach, called interval approach, to type-2 fuzzistics is presented, one that captures the strong points of both the person-MF and interval end-points approaches. It uses interval end-point data that are collected from a group of subjects, assumes a probability distribution for each person's data and maps the mean and standard deviation of that distribution into the parameters of an iteratively specified type-1 person MF. These type-1 person MFs are then aggregated using the union leading to the FOU for a word. Experiments show that this approach is easy to implement and the derived interval type-2 word models match our intuitions, i.e., the FOUs of the small-sounding words are located to the left, the FOUs of the medium-sounding words are located in the middle, and the FOUs of the large-sounding words are located to the right.