Fuzzing has become one of the most popular techniques to identify bugs in software. To improve the fuzzing process, a plethora of techniques have recently appeared in academic literature. However, evaluating and comparing these techniques is challenging as fuzzers depend on randomness when generating test inputs. Commonly, existing evaluations only partially follow best practices for fuzzing evaluations. We argue that the reason for this are twofold. First, it is unclear if the proposed guidelines are necessary due to the lack of comprehensive empirical data in the case of fuzz testing. Second, there does not yet exist a framework that integrates statistical evaluation techniques to enable fair comparison of fuzzers. To address these limitations, we introduce a novel fuzzing evaluation framework called SENF (Statistical EvaluatioN of Fuzzers). We demonstrate the practical applicability of our framework by utilizing the most wide-spread fuzzer AFL as our baseline fuzzer and exploring the impact of different evaluation parameters (e.g., the number of repetitions or run-time), compilers, seeds, and fuzzing strategies. Using our evaluation framework, we show that supposedly small changes of the parameters can have a major influence on the measured performance of a fuzzer.