  We propose a simple robust hypothesis test that has the same sample
complexity as that of the optimal Neyman-Pearson test up to constants, but
robust to distribution perturbations under Hellinger distance. We discuss the
applicability of such a robust test for estimating distributions in Hellinger
distance. We empirically demonstrate the power of the test on canonical
distributions.
