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The author appreciates comments and feedback from Wei Li at the University of California, Irvine, Chongzhi Zang at the University of Virginia, and the author’s PhD student Guanao Yan and postdoc Xinzhou Ge at UCLA. The author was supported by the following grants: National Science Foundation DBI-1846216 and DMS-2113754, NIH/NIGMS R35GM140888, a Johnson & Johnson WiSTEM2D Award, Sloan Research Fellowship, UCLA David Geffen School of Medicine W. M. Keck Foundation Junior Faculty Award, and the Chan-Zuckerberg Initiative Single-Cell Biology Data Insights Grant. The author was a fellow at the Radcliffe Institute for Advanced Study at Harvard University in 2022–2023 while writing this paper.
The author declares no competing interests.
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Li, J.J. How the Monty Hall problem is similar to the false discovery rate in high-throughput data analysis. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01794-9