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Bayesian Statistical Inference for Psychological Research

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Breakthroughs in Statistics

Part of the book series: Springer Series in Statistics ((PSS))

Abstract

Bayesian statistics, a currently controversial viewpoint concerning statistical inference, is based on a definition of probability as a particular measure of the opinions of ideally consistent people. Statistical inference is modification of these opinions in the light of evidence, and Bayes’ theorem specifies how such modifications should be made. The tools of Bayesian statistics include the theory of specific distributions and the principle of stable estimation, which specifies when actual prior opinions may be satisfactorily approximated by a uniform distribution. A common feature of many classical significance tests is that a sharp null hypothesis is compared with a diffuse alternative hypothesis. Often evidence which, for a Bayesian statistician, strikingly supports the null hypothesis leads to rejection of that hypothesis by standard classical procedures. The likelihood principle emphasized in Bayesian statistics implies, among other things, that the rules governing when data collection stops are irrelevant to data interpretation. It is entirely appropriate to collect data until a point has been proven or disproven, or until the data collector runs out of time, money, or patience.

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Author information

Authors and Affiliations

  1. University of Michigan, USA

    Ward Edwards, Harold Lindman & Leonard J. Savage

Authors
  1. Ward Edwards
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  2. Harold Lindman
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  3. Leonard J. Savage
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Editor information

Editors and Affiliations

  1. College of Business and Management, University of Maryland at College Park, College Park, MD, 20742, USA

    Samuel Kotz

  2. Department of Statistics Phillips Hall, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Norman L. Johnson

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© 1992 Springer Science+Business Media New York

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Edwards, W., Lindman, H., Savage, L.J. (1992). Bayesian Statistical Inference for Psychological Research. In: Kotz, S., Johnson, N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0919-5_34

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  • DOI: https://doi.org/10.1007/978-1-4612-0919-5_34

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94037-3

  • Online ISBN: 978-1-4612-0919-5

  • eBook Packages: Springer Book Archive

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