| Description |
1 online resource (1 PDF file (xii, 101 pages)) : illustrations |
| Physical Medium |
polychrome |
| Description |
text file |
| Bibliography |
Includes bibliographical references. |
| 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. |
| Funding |
This 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. |
| 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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Big data -- Congresses.
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Big data. |
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Mathematical statistics -- Congresses.
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Mathematical statistics. |
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Science -- Methodology -- Congresses.
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Science -- Methodology. |
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Experimental design -- Congresses.
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Experimental design. |
| Genre/Form |
Congress.
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Electronic books.
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Conference papers and proceedings.
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Conference papers and proceedings.
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Technical reports.
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Technical reports.
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| Added Author |
National Academies of Sciences, Engineering, and Medicine (U.S.). Committee on Applied and Theoretical Statistics, issuing body.
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Refining the Concept of Scientific Inference When Working with Big Data (Workshop) (2016 : Washington, D.C.)
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| ISBN |
9780309454452 |
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030945445X |
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9780309454445 |
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0309454441 |
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