The problem of detecting a non-Gaussian time series in the presence of additive Gaussian or non-Gaussian noise is cast into a classical hypothesis testing framework, using the sample bispectrum as the test statistic. The power of the test is demonstrated as a function of signal-to-noise ratio, the degree of skewness of the signal, and processing parameters. The results are compared to the power of a classical energy detection test. It is concluded that the bispectrum can be used effectively to detect non-Gaussian signals in the presence of interfering noise and that it may perform better, depending on the degree of non-Gaussianity, than energy detection. >