Reproducibility and Replicability in Science

National Academies of Sciences, Engineering, and Medicine · Policy and Global Affairs · Committee on Science, Engineering, Medicine, and Public Policy · Board on Research Data and Information · Division on Engineering and Physical Sciences · Committee on Applied and Theoretical Statistics · Board on Mathematical Sciences and Analytics · Division on Earth and Life Studies · Nuclear and Radiation Studies Board · Division of Behavioral and Social Sciences and Education · Committee on National Statistics · Board on Behavioral, Cognitive, and Sensory Sciences · Committee on Reproducibility and Replicability in Science

9/2019 · National Academies Press

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One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery.

Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research.

Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science.

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