PT - JOURNAL ARTICLE AU - Eric W. Bridgeford AU - Shangsi Wang AU - Zhi Yang AU - Zeyi Wang AU - Ting Xu AU - Cameron Craddock AU - Jayanta Dey AU - Gregory Kiar AU - William Gray-Roncal AU - Carlo Colantuoni AU - Christopher Douville AU - Stephanie Noble AU - Carey E. Priebe AU - Brian Caffo AU - Michael Milham AU - Xi-Nian Zuo AU - Consortium for Reliability and Reproducibility AU - Joshua T. Vogelstein TI - Eliminating accidental deviations to minimize generalization error and maximize reliability: applications in connectomics and genomics AID - 10.1101/802629 DP - 2020 Jan 01 TA - bioRxiv PG - 802629 4099 - http://biorxiv.org/content/early/2020/12/17/802629.short 4100 - http://biorxiv.org/content/early/2020/12/17/802629.full AB - Reproducibility, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a reproducibility crisis. A key to reproducibility is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing reproducibility statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual’s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the reproducibility crisis, and more generally, mitigating accidental measurement error.Competing Interest StatementThe authors have declared no competing interest.