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
Date
In publication
Volume
2
Issue
2
Resource type
Background/Context
Medium
Print
Background/context type
Conceptual
Open access/free-text available
Yes
Peer reviewed
Yes
ISSN
2053-9517
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
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