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.
- Date
- 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
- 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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