Jamming is an easy to execute attack to which wireless sensor networks are extremely vulnerable. If the application requires reliability, jamming needs to be detected and reported in order to cope with this attack. In this article, we investigate different approaches to identify jamming. Available jamming detection schemes primarily suffer from the usage of fixed thresholds as well as required effort. We adapted a variance-based estimate of signal-tonoise ratio measurements, called significance analysis, to the minor resources and computing efforts of wireless sensor nodes. As a start, we used real measurement data for theoretical analysis of the methods under investigation. Independently of the location of the jamming device, our significance analysis approach provides an immediate indication of jamming and can in theory be run with almost least effort, i.e., with O(14). On top of that, we implemented this approach on our state of the art sensor node and tested it in a real world outdoor setting. Our jamming detection engine monitors the wireless channel with a sampling rate of 10 ms. It returns a jamming detection decision within less than 5 ms while though achieving a detection accuracy in between 84 to 99 percent.