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The Deluge of Spurious Correlations in Big Data
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Too many features will likely lead to an overfitted model containing spurious features.
“too many features will likely lead to an over fi tted model which contains features, which are spurious (Graham et al . 1990; Calude & Longo 2017)”
From Human Judgment in algorithmic loops: Individual justice and automated decision‐making (2022) · cites this paper as Calude & Longo 2017
The discussion following Section 6 says that big databases contain mostly spurious regularities, and Section 9 says mining larger databases increases the risk of misleading correlations. That supports the concern about spurious patterns, but the paper does not establish that adding features leads to an overfitted model; its argument concerns database size, not feature count. (LLM, conf=0.84)
Experimental — accuracy not yet measuredLarge datasets are more likely to produce statistically significant results when comparing means and distributions between populations, as well as spurious correlations between variables, especially for complex, real-world observations.
“Large datasets are more likely to produce statistically significant results when comparing means and distributions between populations, as well as spurious correlations between variables, especially for complex, real-world observations (Calude and Longo 2017.”
From Unethical Numbers? A Meta-analysis of Library Learning Analytics Studies (2019) · cites this paper as Calude and Longo 2017
Section 3 states that larger datasets yield more arbitrary, meaningless correlations, and Section 9 says mining bigger databases increases the risk of spurious regularities. The paper also discusses the difficulty of interpreting patterns in real-life phenomena (section 13). Our copy does not establish the separate claim that larger datasets are more likely to produce statistically significant differences in means or distributions between populations. (LLM, conf=0.88)
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Metadata
- Authors
- Calude, C. S., & Longo, G.
- Affiliations
- University of Auckland (NZ)
- Tufts University (US)
- Corresponding author
- Longo, Giuseppe
- Venue
- Foundations of Science
- Published
- 2016-03-07
- Volume
- 22
- Issue
- 3
- Pages
- 595-612
- ISSN
- 1233-1821 (ISSN-L)
- 1572-8471
- OpenAlex
- W2299251378
- Cited by
- 385
- References
- 0
- Citations by year
- 2026: 14 · 2025: 34 · 2024: 22 · 2023: 38 · 2022: 61 · 2021: 56 · 2020: 43 · 2019: 46 · 2018: 44 · 2017: 16 · 2016: 11
- FWCI
- 35.89
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Foundations of Science (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- ResearchSpace (University of Auckland) (submittedVersion)
- Funders
- FP7 People: Marie-Curie Actions
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- spurious correlations, big data, large databases, data mining, scientific method, end of science, ergodic theory, Ramsey theory, algorithmic information theory
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