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Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call "analysisdoi:10.1371/journal.pbio.3000070 pmid:30532167 pmcid:PMC6301703 fatcat:mh3lk3sdzfcfpkjtl2af57aldm