This document proposes a new statistical framework for modeling and evaluating anonymity in sensor networks. It introduces the concept of "interval indistinguishability" to quantitatively measure anonymity. It also maps source anonymity to the problem of binary hypothesis testing with nuisance parameters. This transforms the problem from analyzing real-valued data to binary codes, allowing coding theory to be applied. Existing solutions can be modified using this framework to improve their level of anonymity.