Refining the concept of scientific inference when working with big data : proceedings of a workshop /

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 h...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wender, Ben A. (rapporteur.)
Autor Corporativo: Refining the Concept of Scientific Inference When Working with Big Data (Workshop)
Formato: Electrónico Congresos, conferencias eBook
Idioma:Inglés
Publicado: Washington (DC) : National Academies Press (US), 2017.
Temas:
Big data > Congresses.
Mathematical statistics > Congresses.
Science > Methodology > Congresses.
Experimental design > Congresses.
Science.
Data sets.
Statistics.
Données volumineuses > Congrès.
Sciences > Méthodologie > Congrès.
Plan d'expérience > Congrès.
Sciences.
Jeux de données.
Statistiques.
Statistique.
sciences (philosophy)
science (modern discipline)
statistics.
MATHEMATICS > Essays.
MATHEMATICS > Pre-Calculus.
MATHEMATICS > Reference.
Statistics
Science
Data sets
Big data
Experimental design
Mathematical statistics
Science > Methodology
Datasets as Topic
Statistics as Topic
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Congress
Acceso en línea:Texto completo
Descripción
Sumario: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.
Descripción Física:1 online resource (1 PDF file (xii, 101 pages)) : illustrations
Bibliografía:Includes bibliographical references.
ISBN:9780309454452
030945445X

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