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Statistics: a data science for the 21st century

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  • Peter J. Diggle

Abstract

type="main" xml:id="rssa12132-abs-0001"> The rise of data science could be seen as a potental threat to the long-term status of the statistics discipline. I first argue that, although there is a threat, there is also a much greater opportunity to re-emphasize the universal relevance of statistical method to the interpretation of data, and I give a short historical outline of the increasingly important links between statistics and information technology. The core of the paper is a summary of several recent research projects, through which I hope to demonstrate that statistics makes an essential, but incomplete, contribution to the emerging field of ‘electronic health’ research. Finally, I offer personal thoughts on how statistics might best be organized in a research-led university, on what we should teach our students and on some issues broadly related to data science where the Royal Statistical Society can take a lead.

Suggested Citation

  • Peter J. Diggle, 2015. "Statistics: a data science for the 21st century," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 793-813, October.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:4:p:793-813
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    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-4
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    Cited by:

    1. Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    2. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Christophe Ley & Yves Dominicy, 2017. "Mutual Point-winning Probabilities (MPW): a New Performance Measure for Table Tennis," Working Papers ECARES ECARES 2017-23, ULB -- Universite Libre de Bruxelles.
    4. Sylvia Richardson, 2022. "Statistics in times of increasing uncertainty," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1471-1496, October.
    5. Jenny Farmer & Donald Jacobs, 2018. "High throughput nonparametric probability density estimation," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-29, May.

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