IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v136y2018icp142-145.html
   My bibliography  Save this article

When small data beats big data

Author

Listed:
  • Faraway, Julian J.
  • Augustin, Nicole H.

Abstract

Small data is sometimes preferable to big data. A high quality small sample can produce superior inferences to a low quality large sample. Data has acquisition, computation and privacy costs which require costs to be balanced against benefits. Statistical inference works well on small data but not so well on large data. Sometimes aggregation into small datasets is better than large individual-level data. Small data is a better starting point for teaching of Statistics.

Suggested Citation

  • Faraway, Julian J. & Augustin, Nicole H., 2018. "When small data beats big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 142-145.
    • Handle: RePEc:eee:stapro:v:136:y:2018:i:c:p:142-145
    DOI: 10.1016/j.spl.2018.02.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715218300762
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2018.02.031?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Secchi, Piercesare, 2018. "On the role of statistics in the era of big data: A call for a debate," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 10-14.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Bowman, Adrian W., 2018. "Big questions, informative data, excellent science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 34-36.
    2. Reid, Nancy, 2018. "Statistical science in the world of big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 42-45.
    3. Daniel Amo & Sandra Cea & Nicole Marie Jimenez & Pablo Gómez & David Fonseca, 2021. "A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
    4. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    5. Schmalz, Ulrike & Ringbeck, Jürgen & Spinler, Stefan, 2021. "Door-to-door air travel: Exploring trends in corporate reports using text classification models," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Sharina Tajul Urus & Intan Waheedah Othman & Zarinah Abdul Rasit & Noraizah Abu Bakar & Sharifah Nazatul Faiza Syed Mustapha Nazri, 2023. "Beyond the Hype of Big Data Analytics Deployment: Conceptualization and Challenges Epistemology," Business and Economic Research, Macrothink Institute, vol. 13(2), pages 74-111, December.
    7. McKercher, Bob & Tkaczynski, Aaron, 2023. "Valuation of travel time," Annals of Tourism Research, Elsevier, vol. 100(C).
    8. Wang, Wenting & Shi, Shijie & Fu, Xianghua, 2022. "The subnetwork investigation of scale-free networks based on the self-similarity," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).

    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.
    1. Bühlmann, Peter & van de Geer, Sara, 2018. "Statistics for big data: A perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 37-41.
    2. Nadeem Shafique Butt & Ahmad Azam Malik & Muhammad Qaiser Shahbaz, 2021. "Bibliometric Analysis of Statistics Journals Indexed in Web of Science Under Emerging Source Citation Index," SAGE Open, , vol. 11(1), pages 21582440209, January.
    3. Ceri, Stefano, 2018. "On the role of statistics in the era of big data: A computer science perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 68-72.
    4. Torrecilla, José L. & Romo, Juan, 2018. "Data learning from big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 15-19.
    5. Azzone, Giovanni, 2018. "Big data and public policies: Opportunities and challenges," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 116-120.

    More about this item

    Keywords

    Big data; Small data;

    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:eee:stapro:v:136:y:2018:i:c:p:142-145. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.