In this paper, we study challenging anomaly detections in streaming videos under fully unsupervised settings. Unsupervised unmasking methods [12] have recently been applied to anomaly detection; however, the theoretical understanding of it is still limited. Aiming to understand and improve this method, we propose a novel perspective to establish the connection between the heuristic unmasking procedure and multiple classifier two sample tests (MC2ST) in statistical machine leaning. Based on our analysis of the testing power of MC2ST, we present a history sampling method to increase the testing power as well as to improve the performance on video anomaly detection. We also offer a new frame-level motion feature that has better representation and generalization ability, and obtain improvement on several video benchmark datasets. The code could be found at https://github. com/MYusha/Video-Anomaly-Detection.