Mutation testing is often used to assess the quality of a test suite by analyzing its ability to distinguish between a base program and its mutants. The main threat to the validity/reliability of this assessment approach is that many mutants may be syntactically distinct from the base, yet functionally equivalent to it. The problem of identifying equivalent mutants and excluding them from consideration is the focus of much recent research. In this paper we argue that it is not necessary to identify individual equivalent mutants and count them; rather it is sufficient to estimate their number. To do so, we consider the question: what makes a program prone to produce equivalent mutants? Our answer is: redundancy does. Consequently, we introduce a number of program metrics that capture various dimensions of redundancy in a program, and show empirically that they are statistically linked to the rate of equivalent mutants.