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Data Mining: A Reconsideration

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  • Thomas Mayer

Abstract

Before condemning data mining one should distinguish between objective and biased data mining. The former is commendable. Even biased data mining is appropriate when used to illustrate and not to test hypotheses. In the context of testing, the problem with biased data mining arises not from the fitting of many regression, but from inadequate reporting of results. The trend towards reporting the results of more alternative specifications, and thus addressing the fragility problem, should be encouraged. To do that the incentives that economists face should be changed.

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  • Thomas Mayer, "undated". "Data Mining: A Reconsideration," Department of Economics 97-15, California Davis - Department of Economics.
    • Handle: RePEc:fth:caldec:97-15
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    File URL: http://www.econ.ucdavis.edu/working_papers/97-15.pdf
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    Cited by:

    1. Gérard Charreaux, 2008. "La recherche en finance d’entreprise:quel positionnement méthodologique ?," Revue Finance Contrôle Stratégie, revues.org, vol. 11(Special), pages 237-290, June.
    2. Qianjin Zhang & Junjie Lin & Tianyang Liu & Guang Chen, 2022. "Hybridization of Chinese international development volunteering: Evidence from three state‐funded programmes," Development Policy Review, Overseas Development Institute, vol. 40(1), January.
    3. David Colander, 2000. "New Millennium Economics: How Did It Get This Way, and What Way Is It?," Journal of Economic Perspectives, American Economic Association, vol. 14(1), pages 121-132, Winter.
    4. Amavilah, Voxi Heinrich, 2012. "The Caldwellian Methodological Pluralism: Wishful Thoughts and Personal Tendencies," MPRA Paper 44656, University Library of Munich, Germany, revised 28 Feb 2013.

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