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Refining the concept of scientific inference when working with big data : proceedings of a workshop / Ben A. Wender, rapporteur ; Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and their Applications, Division on Engineering and Physical Sciences, the National Academies of Sciences, Engineering, Medicine.

Author/creator Wender, Ben A. rapporteur.
Other author/creatorNational Academies of Sciences, Engineering, and Medicine (U.S.). Committee on Applied and Theoretical Statistics issuing body.
Other author/creatorRefining the Concept of Scientific Inference When Working with Big Data (Workshop) (2016 : Washington, D.C.)
Format Electronic and Book
Publication Info Washington (DC) : National Academies Press (US), 2017.
Description1 online resource (1 PDF file (xii, 101 pages)) : illustrations
Supplemental Content Direct link to resource
Contents Introduction -- Framing the workshop -- Inference about discoveries basedon integration of diverse data sets -- Inference about causal discoveries driven by large observational data -- Inference when regularization is used to simplify fitting of high-dimensional models -- Panel discussion -- References -- Appendixes.
Summary The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop.
Bibliography noteIncludes bibliographical references.
Funding informationThis workshop was supported by Contract No. HHSN26300076 with the National Institutes of Health and Grant No. DMS-1351163 from the National Science Foundation. Any opinions, findings, or conclusions expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.
Source of descriptionOnline resource; title from PDF title page (viewed April 28, 2017).
Issued in other formPrint version: National Academies of Sciences, Engineering, and Medicine. Refining the concept of scientific inference when working with big data : proceedings of a workshop. Washington, District of Columbia : The National Academies Press, ©2017 xii, 101 pages 9780309454445

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