Statistical discrimination is a form of discrimination that uses statistical inferences about the groups to which individuals belong as grounds for treating them differently. It remains unclear what, if anything, makes statistical discrimination wrong. My thesis argues that statistical discrimination is wrong because, and insofar as, it contributes to existing social injustice. After an introduction to the issues in section 1, section 2 clarifies the concept of statistical discrimination and its differences with non-statistical discrimination. Section 3 discusses different accounts that seek to explain when and why statistical discrimination is wrong. I examine two approaches, one of which regards the wrong of statistical discrimination as part of discrimination in general, while the other conceives of the wrong as distinctive to statistical discrimination itself. I argue the former approach is better. Among different accounts within the approach, the context-based consequentialist account that explains the wrong in relation to existing social injustice is most promising. Section 4 uses racial profiling as an example to illustrate how the account can help explain this hard case of statistical discrimination, calling upon an argument developed by Benjamin Eidelson.