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In this article, we seek to better understand biases in search and retrieval, and in particular those affecting the accuracy of content in search results, including the search engine index, features used for ranking, and the formulation of search queries. Focusing on the important domain of online health search, this research broadens previous work on biases in search to examine the role of search systems in contributing to biases. To assess bias, we focus on questions about medical interventions and employ reliable ground truth data from authoritative medical sources. In the course of our study, we utilize large-scale log analysis using data from a popular Web search engine, deep probes of result lists on that search engine, and crowdsourced human judgments of search result captions and landing pages. Our findings reveal bias in results, amplifying searchers' existing biases that appear evident in their search activity. We also highlight significant bias in indexed content and show that specific ranking signals and specific query terms support bias. Both of these can degrade result accuracy and increase skewness in search results. 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