IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v156y1993i2p167-194.html
   My bibliography  Save this article

Predictability and Prediction

Author

Listed:
  • A. S. C. Ehrenberg
  • J. A. Bound

Abstract

A result can be regarded as routinely predictable when it has recurred consistently under a known range of different conditions. This depends on the previous analysis of many sets of data, drawn from different populations. There is no such basis of extensive experience when a prediction is derived from the analysis of only a single set of data. Yet that is what is mainly discussed in our statistical texts. The paper discusses the design and analysis of studies aimed at achieving routinely predictable results. It uses two running case history examples.

Suggested Citation

  • A. S. C. Ehrenberg & J. A. Bound, 1993. "Predictability and Prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(2), pages 167-194, March.
    • Handle: RePEc:bla:jorssa:v:156:y:1993:i:2:p:167-194
    DOI: 10.2307/2982727
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2982727
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2982727?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
    2. Gavin Lees & Maxwell Winchester & Sidath Silva, 2016. "Demographic product segmentation in financial services products in Australia and New Zealand," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(3), pages 240-250, September.
    3. Paul B Conn & Devin S Johnson & Peter L Boveng, 2015. "On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.
    4. Hubbard, Raymond & Lindsay, R. Murray, 2013. "The significant difference paradigm promotes bad science," Journal of Business Research, Elsevier, vol. 66(9), pages 1393-1397.
    5. Salisu, Afees A. & Olaniran, Abeeb & Tchankam, Jean Paul, 2022. "Oil tail risk and the tail risk of the US Dollar exchange rates," Energy Economics, Elsevier, vol. 109(C).
    6. Jella Pfeiffer & Thies Pfeiffer & Martin Meißner & Elisa Weiß, 2020. "Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments," Information Systems Research, INFORMS, vol. 31(3), pages 675-691, September.
    7. Phua, Peilin & Kennedy, Rachel & Trinh, Giang & Page, Bill & Hartnett, Nicole, 2020. "Examining older consumers’ loyalty towards older brands in grocery retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    8. Page, Bill & Sharp, Anne & Lockshin, Larry & Sorensen, Herb, 2018. "Parents and children in supermarkets: Incidence and influence," Journal of Retailing and Consumer Services, Elsevier, vol. 40(C), pages 31-39.
    9. Zachary Anesbury & Maxwell Winchester & Rachel Kennedy, 2017. "Brand user profiles seldom change and seldom differ," Marketing Letters, Springer, vol. 28(4), pages 523-535, December.
    10. 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.
    11. Hubbard, Raymond & Vetter, Daniel E., 1996. "An empirical comparison of published replication research in accounting, economics, finance, management, and marketing," Journal of Business Research, Elsevier, vol. 35(2), pages 153-164, February.
    12. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.
    13. Uncles, Mark D. & Kwok, Simon, 2013. "Designing research with in-built differentiated replication," Journal of Business Research, Elsevier, vol. 66(9), pages 1398-1405.
    14. R. Murray Lindsay, 1994. "Publication System Biases Associated with the Statistical Testing Paradigm," Contemporary Accounting Research, John Wiley & Sons, vol. 11(1), pages 33-57, June.
    15. Hubbard, Raymond & Lindsay, R. Murray, 2013. "From significant difference to significant sameness: Proposing a paradigm shift in business research," Journal of Business Research, Elsevier, vol. 66(9), pages 1377-1388.
    16. Gaunt, J. L. & Riley, Janet & Stein, A. & Penning de Vries, F. W. T., 1997. "Requirements for effective modelling strategies," Agricultural Systems, Elsevier, vol. 54(2), pages 153-168, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:156:y:1993:i:2:p:167-194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.