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Big Data and the Danger of Being Precisely Inaccurate

Item

Title
Big Data and the Danger of Being Precisely Inaccurate
Abstract/Description
Social scientists and data analysts are increasingly making use of Big Data in their analyses. These data sets are often ?found data? arising from purely observational sources rather than data derived under strict rules of a statistically designed experiment. However, since these large data sets easily meet the sample size requirements of most statistical procedures, they give analysts a false sense of security as they proceed to focus on employing traditional statistical methods. We explain how most analyses performed on Big Data today lead to ?precisely inaccurate? results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size. Before any analyses are performed on large data sets, we recommend employing a simple data segmentation technique to control for some major components of observational data biases. These segments will help to improve the accuracy of the results.
Author/creator
McFarland, Daniel A
McFarland, H Richard
Date
2015
In publication
Big Data & Society
Volume
2
Issue
2
Resource type
en Background/Context
Medium
en Print
Background/context type
en Conceptual
Open access/free-text available
en Yes
Peer reviewed
en Yes
Language
en
ISSN
2053-9517
DOI
10.1177/2053951715602495
URL
Open Access Publisher's Webpage (SAGE Journals)
Citation
McFarland, D. A., & McFarland, H. R. (2015). Big Data and the Danger of Being Precisely Inaccurate. Big Data & Society, 2(2), 2053951715602495. https://doi.org/10.1177/2053951715602495
Item sets
Handbook Chapter 19 Citations

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