In the named entity normalization task, a system identifies a canonical unambiguous referent for names like "Bush" or "Alabama." Resolving synonymy and ambiguity of such names can benefit end-to-end information access tasks. We evaluate two entity normalization methods based on Wikipedia in the context of both passage and document retrieval for question answering. We find that even a simple normalization method leads to improvements of early precision, both for document and passage retrieval. Moreover, better normalization results in better retrieval performance.