Identifying falsified clinical data
Clinical data serve as a necessary basis for medical decisions. Consequently, the importance of methods that help officials quickly identify human tampering of data cannot be underestimated. In this paper, we suggest Benford’s Law as a basis for objectively identifying the presence of experimenter distortions in the outcome of clinical research data. We test this tool on a clinical data set that contains falsified data and discuss the implications of using this and information-theoretic methods as a basis for identifying data manipulation and fraud.