Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities. However, they fail to make use of abnormal event patterns, which are elements to comprise abnormal events. Moreover, normal patterns in testing videos may be divergent from training ones, due to the existence of abnormalities. To address these problems, in this paper, an abnormality detector is proposed to detect abnormal events based on a statistical hypothesis test. The proposed detector treats each sample as a combination of a set of event patterns. Due to the unavailability of labeled abnormalities for training, abnormal patterns are adaptively extracted from incoming unlabeled testing samples. Contributions of this paper are listed as follows: 1) we introduce the idea of a statistical hypothesis test into the framework of abnormality detection, and abnormal events are identified as ones containing abnormal event patterns while possessing high abnormality detector scores; 2) due to the complexity of video events, noise seldom follows a simple distribution. For this reason, we approximate the complex noise distribution by employing a mixture of Gaussian. This benefits the modeling of video events and improves abnormality detection accuracies; and 3) because of the existence of abnormalities, there are always some unusually occurring normal events in the testing videos, which differ from the training ones. To represent normal events precisely, an online updating strategy is proposed to cover these cases in the normal event patterns. As a result, false detections are eliminated mostly. Extensive experiments and comparisons with state-of-the-art methods verify the effectiveness of the proposed algorithm.