Digital watermarking to form fingerprints in databases is an important approach for database right protection and ownership identification. It provides protection for the sharing and fusion of data. As existing database fingerprinting methods have a deficiency in the universality of data, this study proposes a database fingerprinting approach based on statistical features. This approach first divides the host data into several subsets by an iterative hash function. Then, the statistical feature of each subset is maximized/minimized by an optimization algorithm after extreme values are filtered out. Finally, the optimum threshold is taken as fingerprint information which is calculated by Bayesian decision for minimum errors. This study also theoretically verifies the feasibility and effectiveness of the proposed method. The experimental results on real datasets demonstrate that the method has advantages in both robustness and universality.