Reproducibility and Replicability in Science (2019) / Chapter Skim
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Executive Summary
Executive Summary
Pages 1-4
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We define reproducibility to mean computational reproducibility -- obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis; and replicability to mean obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data. In short, reproducibility involves the original data and code; replicability involves new data collection and similar methods used by previous studies.
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To help remedy these problems, NSF should, in harmony with other funders, endorse or create code and data repositories for the long-term preservation of digital artifacts. In line with its expressed goal of "harnessing the data revolution," NSF should consider funding tools, training, and activities to promote computational reproducibility.
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Importantly, the assessment of replicability may not result in a binary pass/fail answer; rather, the answer may best be expressed as the degree to which one result replicates another. One type of scientific research tool, statistical inference, has had an outsized role in replicability discussions due to the frequent misuse of statistics such as the p-value and threshold for determining statistical significance.
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