This paper presents a technique for computing and classifying data dependences that takes into account the complexities introduced by specific language constructs, such as pointers, arrays, and structures. The classification is finer-grained than previously proposed classifications. Moreover, unlike previous work, the paper presents empirical results that illustrate the distribution of data dependences for a set of C subjects. The paper also presents a potential application for the proposed classification---program slicing---and a technique that computes slices based on data-dependence types. This technique facilitates the use of slicing for program understanding because a user can either augment a slice incrementally, by incorporating data dependences based on their relevance, or focus on specific kinds of dependences. Finally, the paper presents a case study that shows how the incremental addition of data dependences allows for growing the size of the slices in steps.