IDEAS home Printed from https://ideas.repec.org/a/vrs/foeste/v15y2015i2p144-152n10.html
   My bibliography  Save this article

Statistical Computing in Information Society

Author

Listed:
  • Domański Czesław

    (University of Lodz, Faculty of Economics and Sociology, Institute of Statistics and Demography, POW 3/5, 90-255 Łódź, Poland)

  • Jędrzejczak Alina

    (Centre of Mathematical Statistics, Statistical Office in Łódź Suwalska 29, 93-176 Lódź, Poland)

Abstract

In the presence of massive data coming with high heterogeneity we need to change our statistical thinking and statistical education in order to adapt both - classical statistics and software developments that address new challenges. Significant developments include open data, big data, data visualisation, and they are changing the nature of the evidence that is available, the ways in which it is presented and the skills needed for its interpretation. The amount of information is not the most important issue – the real challenge is the combination of the amount and the complexity of data. Moreover, a need arises to know how uncertain situations should be dealt with and what decisions should be taken when information is insufficient (which can also be observed for large datasets). In the paper we discuss the idea of computational statistics as a new approach to statistical teaching and we try to answer a question: how we can best prepare the next generation of statisticians.

Suggested Citation

  • Domański Czesław & Jędrzejczak Alina, 2015. "Statistical Computing in Information Society," Folia Oeconomica Stetinensia, Sciendo, vol. 15(2), pages 144-152, December.
    • Handle: RePEc:vrs:foeste:v:15:y:2015:i:2:p:144-152:n:10
    DOI: 10.1515/foli-2015-0041
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/foli-2015-0041
    Download Restriction: no
    File URL: https://libkey.io/10.1515/foli-2015-0041?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
    ---><---

    References listed on IDEAS

    as
    1. Lauro, Carlo, 1996. "Computational statistics or statistical computing, is that the question?," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 191-193, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:vrs:foeste:v:15:y:2015:i:2:p:144-152:n:10. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

      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.