The Deluge of Spurious Correlations in Big Data


Very large databases are a major opportunity for science and data analytics is a remarkable new field of investigation in computer science. The effectiveness of these tools is used to support a “philosophy” against the scientific method as developed throughout history. According to this view, computer-discovered correlations should replace understanding and guide prediction and action. Consequently, there will be no need to give scientific meaning to phenomena, by proposing, say, causal relations, since regularities in very large databases are enough: “with enough data, the numbers speak for themselves”. The “end of science” is proclaimed. Using classical results from ergodic theory, Ramsey theory and algorithmic information theory, we show that this “philosophy” is wrong. For example, we prove that very large databases have to contain arbitrary correlations. These correlations appear only due to the size, not the nature, of data. They can be found in “randomly” generated, large enough databases, which—as we will prove—implies that most correlations are spurious. Too much information tends to behave like very little information. The scientific method can be enriched by computer mining in immense databases, but not replaced by it.

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  1. 1.

    Anderson attributed the last sentence to Google’s research director Peter Norvig (2008), who denied it: “That’s a silly statement, I didn’t say it, and I disagree with it.”

  2. 2.

    See more in Andrews (2012).

  3. 3.

    This example points to another important issue: no data collecting is strictly objective—see the analysis in Grjebine (2015) of Reinhart and Rogoff’s bias in their collection of data in several countries for 218 years.

  4. 4.

    European policy makers largely referred to that paper till 2013. For example, O. Rehn, EU Commissioner for Economic Affairs (2009–13) referred to the Reinhart-Rogoff correlation as a key guideline for his past and present economic views (Smith (2013), address to ILO, April 9, 2013) and G. Osborne, British Chancellor of the Exchequer (since 2010), claimed in April 2013: “As Rogoff and Reinhart demonstrate convincingly, all financial crises ultimately have their origins in one thing [the public debt].” (Lyons 2013).

  5. 5.

    See also the huge collection of spurious correlations (Spurious 2015) and the book (Vigen 2015) based on it, in which the old rule that “correlation does not equal causation” is illustrated through hilarious graphs.

  6. 6.

    This was informally observed also in Smith (2014, p. 20): “With fast computers and plentiful data, finding statistical relevance is trivial. If you look hard enough, it can even be found in tables of random numbers”.

  7. 7.

    They are branches of finite combinatorics and the theory of algorithms, respectively.

  8. 8.

    A branch of mathematics which studies dynamical systems with an invariant measure and related problems.

  9. 9.

    The measure of A, \(\mu (A)\), is the probability of A.

  10. 10.

    Ehrenfest’s example (Walkden 2010) is a simple illustration. Let an urn \(U_1\) contain 100 numbered balls and \(U_2\) be an empty urn. Each second, one ball is moved from one urn to the other, according to the measurement of events that produce numbers from 1 to 100. By Kac’s lemma, the expected return time to (almost) all balls in \(U_1\) is of (nearly) \(2^{100}\) s, which is about \(3 \times 10^{12}\) times the age of the Universe. Boltzmann already had an intuition of this phenomenon, in the study of the recurrence time in ergodic dynamics, of gas particles for example, see Cecconi et al. (2012).

  11. 11.

    The dimension of an attractor is the number of effective degrees of freedom.

  12. 12.

    Our footnote: see Lynch (2008), Chibbaro et al. (2014).

  13. 13.

    For example, with one dimension far exceeding the age of the Universe in seconds, yotta of yottabytes.

  14. 14.

    Appeared in Putnam Mathematical Competition in 1953 and in the problem section of the American Mathematical Monthly in 1958 (Problem E 1321).

  15. 15.

    This is a Ramsey type problem: the aim is to find out how large the party needs to be to guarantee similar pairwise acquaintanceship in (at least) one group of three people.

  16. 16.

    The function K is incomputable.

  17. 17.

    Traditionally, \(K_U\) is called Kolmogorov complexity associated to U.

  18. 18.

    The number of strings x of length n having \(K(x) \ge n-m\) is greater or equal to \(2^{n}-2^{n-m}+1\).

  19. 19.

    In view of results discussed in Sect. 6, they cannot have all properties associated with randomness.

  20. 20.

    For every x, Zip(x) is an incompressible string for Zip, but for some x, Zip(x) is compressible by U.

  21. 21.

    \(10^{82}\) is approximately the number of hydrogen atoms in the observable Universe.

  22. 22.

    Latin: with this, therefore because of this.

  23. 23.

    Latin: after this, therefore because of this.

  24. 24.

    The danger of purely speculative theories in today’s physics is discussed in Ellis and Silk (2014).

  25. 25.

    The big data can be used for scientific testing of hypotheses as well as for testing scientific theories and results.


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The authors have been supported in part by Marie Curie FP7-PEOPLE-2010-IRSES Grant. Longo’s work is also part of the project “Lois des dieux, des hommes et de la nature”, Institut d’Etudes Avancées, Nantes, France. We thank A. Vulpiani for suggesting the use of Kac’s lemma, G. Tee for providing historical data and A. Abbott, F. Kroon, H. Maurer, J. P. Lewis, C. Mamali, R. Nicolescu, G. Smith, G. Tee, A. Vulpiani and the anonymous referees for useful comments and suggestions.

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Correspondence to Giuseppe Longo.

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Italian: were not born to live like brutes, but to pursue virtue and knowledge.

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Calude, C.S., Longo, G. The Deluge of Spurious Correlations in Big Data. Found Sci 22, 595–612 (2017).

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  • Big data
  • Ergodic theory
  • Ramsey theory
  • Algorithmic information theory
  • Correlation