spurious correlation


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spurious correlation

[′spyu̇r·ē·əs ‚kä·rə′lā·shən]
(statistics)
The value of the coefficient of correlation when it is computed correctly but its relationship implications are nonsensical or unreasonable.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

spurious correlation

see MULTIVARIATE ANALYSIS.
Collins Dictionary of Sociology, 3rd ed. © HarperCollins Publishers 2000
References in periodicals archive ?
The high correlation of minorities to other socioeconomic factors may be causing the high but apparently spurious correlation with new sites and pollution, if the socioeconomic factor in question is a significant variable in explaining the location of new sites.
Pearson (1897) discussed the problem of spurious correlations with the use of indices that comprise a part-whole relationship.
Therefore, they disregarded the contention of spurious correlation to the BKS study.
But it is important that by no means all of these Ns will themselves be objectively correlated with the treatment T, and so not all of them will be capable of producing a spurious correlation. It is specifically Ns which are correlated with T which threaten this, and it is specifically these possibly confounding Ns that experimental randomization guards against.
Comments on "Some misconceptions about the spurious correlation problem in the ecological literature" by Y.
A website called Spurious Correlations - set up by Harvard law student Tyler Vigen - details countless hilarious graphs demonstrating this fact.
Prewhitening removes spurious correlations based on temporal dependencies between adjacent values of the input time series and it removes these influences from the output time series.
What is the future of so much reliance on data, where a lot of spurious correlations could dominate our lifestyle and livelihood?
You can always cherry-pick spurious correlations as examples, but analysing the full gamut of corporate headquarters yields a non-signal.
Indeed, my favorite phrase in his article is: "small samples have been mined for spurious correlations in support of powerful pre-existing biases [in regard to bats], while researchers ignored evidence that pointed in the opposite direction."
DDDM models are also vulnerable to poor design, he said, such as overfitting, spurious correlations and the identification of inappropriate criteria.