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Making Statistical Methods More Useful

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  • Michael Wood

Abstract

I present a critique of the methods used in a typical article. This leads to three broad conclusions about the conventional use of statistical methods. First, results are often reported in an unnecessarily obscure manner. Second, the null hypothesis testing paradigm is deeply flawed: Estimating the size of effects and citing confidence intervals or levels is usually better. Third, there are several issues, independent of the particular statistical concepts employed, which limit the value of any statistical approach—for example, difficulties of generalizing to different contexts and the weakness of some research in terms of the size of the effects found. The first two of these are easily remedied—I illustrate some of the possibilities by reanalyzing the data from the case study article—and the third means that in some contexts, a statistical approach may not be worthwhile. My case study is a management article, but similar problems arise in other social sciences.

Suggested Citation

  • Michael Wood, 2013. "Making Statistical Methods More Useful," SAGE Open, , vol. 3(1), pages 21582440134, February.
    • Handle: RePEc:sae:sagope:v:3:y:2013:i:1:p:2158244013476873
    DOI: 10.1177/2158244013476873
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    References listed on IDEAS

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    1. Michael Wood & Richard Christy, 1999. "Sampling for Possibilities," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(2), pages 185-202, May.
    2. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, April.
    3. Lindsay, R. Murray, 1995. "Reconsidering the status of tests of significance: An alternative criterion of adequacy," Accounting, Organizations and Society, Elsevier, vol. 20(1), pages 35-53, January.
    4. M Wood & M Kaye & N Capon, 1999. "The use of resampling for estimating control chart limits," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(6), pages 651-659, June.
    5. J Mingers, 2006. "A critique of statistical modelling in management science from a critical realist perspective: its role within multimethodology," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(2), pages 202-219, February.
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    Cited by:

    1. Pierre-Olivier Bédard, 2015. "The Mobilization of Scientific Evidence by Public Policy Analysts," SAGE Open, , vol. 5(3), pages 21582440156, September.

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