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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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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

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THE NATIONAL ACADEMIES PRESS
Washington, DC
www.nap.edu

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×

THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001

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.

International Standard Book Number-13: 978-0-309-45444-5
International Standard Book Number-10: 0-309-45444-1
Digital Object Identifier: 10.17226/24654

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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Reports document the evidence-based consensus of an authoring committee of experts. Reports typically include findings, conclusions, and recommendations based on information gathered by the committee and committee deliberations. Reports are peer reviewed and are approved by the National Academies of Sciences, Engineering, and Medicine.

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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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PLANNING COMMITTEE ON REFINING THE CONCEPT OF SCIENTIFIC INFERENCE WHEN WORKING WITH BIG DATA

MICHAEL J. DANIELS, University of Texas, Austin, Co-Chair

ALFRED O. HERO III, University of Michigan, Co-Chair

GENEVERA ALLEN, Rice University and Baylor College of Medicine

CONSTANTINE GATSONIS, Brown University

GEOFFREY GINSBURG, Duke University

MICHAEL I. JORDAN, NAS1/NAE,2 University of California, Berkeley

ROBERT E. KASS, Carnegie Mellon University

MICHAEL KOSOROK, University of North Carolina, Chapel Hill

RODERICK J.A. LITTLE, NAM,3 University of Michigan

JEFFREY S. MORRIS, MD Anderson Cancer Center

RONITT RUBINFELD, Massachusetts Institute of Technology

Staff

MICHELLE K. SCHWALBE, Board Director

BEN A. WENDER, Associate Program Officer

LINDA CASOLA, Staff Editor

RODNEY N. HOWARD, Administrative Assistant

ELIZABETH EULLER, Senior Program Assistant

___________________

1 National Academy of Sciences.

2 National Academy of Engineering.

3 National Academy of Medicine.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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COMMITTEE ON APPLIED AND THEORETICAL STATISTICS

CONSTANTINE GATSONIS, Brown University, Chair

DEEPAK AGARWAL, LinkedIn

MICHAEL J. DANIELS, University of Texas, Austin

KATHERINE BENNETT ENSOR, Rice University

MONTSERRAT (MONTSE) FUENTES, North Carolina State University

ALFRED O. HERO III, University of Michigan

AMY HERRING, University of North Carolina, Chapel Hill

DAVID M. HIGDON, Social Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech

ROBERT E. KASS, Carnegie Mellon University

JOHN LAFFERTY, University of Chicago

JOSÉ M.F. MOURA, NAE, Carnegie Mellon University

SHARON-LISE T. NORMAND, Harvard University

ADRIAN RAFTERY, NAS, University of Washington

LANCE WALLER, Emory University

EUGENE WONG, NAE, University of California, Berkeley

Staff

MICHELLE K. SCHWALBE, Director

LINDA CASOLA, Research Associate and Staff Writer/Editor

BETH DOLAN, Financial Associate

RODNEY N. HOWARD, Administrative Assistant

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×

BOARD ON MATHEMATICAL SCIENCES AND THEIR APPLICATIONS

DONALD SAARI, NAS, University of California, Irvine, Chair

DOUGLAS N. ARNOLD, University of Minnesota

JOHN B. BELL, NAS, Lawrence Berkeley National Laboratory

VICKI M. BIER, University of Wisconsin, Madison

JOHN R. BIRGE, NAE, University of Chicago

RONALD COIFMAN, NAS, Yale University

L. ANTHONY COX, JR., NAE, Cox Associates, Inc.

MARK L. GREEN, University of California, Los Angeles

PATRICIA A. JACOBS, Naval Postgraduate School

BRYNA KRA, Northwestern University

JOSEPH A. LANGSAM, Morgan Stanley (retired)

SIMON LEVIN, NAS, Princeton University

ANDREW W. LO, Massachusetts Institute of Technology

DAVID MAIER, Portland State University

WILLIAM A. MASSEY, Princeton University

JUAN C. MEZA, University of California, Merced

FRED S. ROBERTS, Rutgers University

GUILLERMO R. SAPIRO, Duke University

CARL P. SIMON, University of Michigan

KATEPALLI SREENIVASAN, NAS/NAE, New York University

ELIZABETH A. THOMPSON, NAS, University of Washington

Staff

MICHELLE K. SCHWALBE, Board Director

NEAL GLASSMAN, Senior Program Officer

LINDA CASOLA, Research Associate and Staff Writer/Editor

BETH DOLAN, Financial Associate

RODNEY N. HOWARD, Administrative Assistant

Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×

Acknowledgment of Reviewers

This proceedings has been reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the institution in making its published proceedings as sound as possible and to ensure that the proceedings meets institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We wish to thank the following individuals for their review of this proceedings:

Joseph Hogan, Brown University,

Iain Johnstone, NAS, Stanford University,

Xihong Lin, Harvard University, and

Hal Stern, University of California, Irvine.

Although the reviewers listed above have provided many constructive comments and suggestions, they were not asked to endorse the views presented at the workshop, nor did they see the final draft of the workshop proceedings before its release. The review of this workshop proceedings was overseen by Sallie Keller, Social Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech, who was responsible for making certain that an independent examination of this workshop proceedings was carried out in accordance with institutional procedures and that all review comments were carefully considered. Responsibility for the final content of this proceedings rests entirely with the rapporteur and the institution.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
×
Page R9
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Page R10
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24654.
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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.

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