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.
Original languageEnglish
Pages (from-to)142-145
Number of pages4
JournalStatistics & Probability Letters
Volume136
Early online date17 Feb 2018
DOIs
Publication statusPublished - 1 May 2018

Fingerprint

Beat
Large Data
Costs
Statistical Inference
Data Acquisition
Small Sample
Privacy
Aggregation
Statistics
Inference
Statistical inference
Small sample

Keywords

  • Big data
  • Small data

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

When small data beats big data. / Faraway, Julian; Augustin, Nicole.

In: Statistics & Probability Letters, Vol. 136, 01.05.2018, p. 142-145.

Research output: Contribution to journalArticle

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