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Journal articles on the topic 'Spurious correlations'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 2025

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1

Ben-Zeev, Talia, and Jon R. Star. "Spurious Correlations in Mathematical Thinking." Cognition and Instruction 19, no. 3 (2001): 253–75. http://dx.doi.org/10.1207/s1532690xci1903_1.

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2

Ward, Andrew. "“Spurious Correlations and Causal Inferences”." Erkenntnis 78, no. 3 (2012): 699–712. http://dx.doi.org/10.1007/s10670-012-9411-6.

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3

Jackson, D. A., and K. M. Somers. "The spectre of ?spurious? correlations." Oecologia 86, no. 1 (1991): 147–51. http://dx.doi.org/10.1007/bf00317404.

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4

Halperin, Silas. "Spurious correlations—causes and cures." Psychoneuroendocrinology 11, no. 1 (1986): 3–13. http://dx.doi.org/10.1016/0306-4530(86)90028-4.

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5

Lorenzo-Arribas, Altea, Penny S. Reynolds, and Chaitra H. Nagaraja. "Suffrage, Statistics, and Spurious Correlations." CHANCE 36, no. 4 (2023): 51–54. http://dx.doi.org/10.1080/09332480.2023.2290956.

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6

Fan, Jianqing, Qi-Man Shao, and Wen-Xin Zhou. "Are discoveries spurious? Distributions of maximum spurious correlations and their applications." Annals of Statistics 46, no. 3 (2018): 989–1017. http://dx.doi.org/10.1214/17-aos1575.

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7

Berges, John A. "Ratios, regression statistics, and “spurious” correlations." Limnology and Oceanography 42, no. 5 (1997): 1006–7. http://dx.doi.org/10.4319/lo.1997.42.5.1006.

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8

Pollman, Curtis D., and Donald M. Axelrad. "Mercury bioaccumulation factors and spurious correlations." Science of The Total Environment 496 (October 2014): vi—xii. http://dx.doi.org/10.1016/j.scitotenv.2014.07.050.

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9

Richman, Jesse T., and Ryan J. Roberts. "Assessing Spurious Correlations in Big Search Data." Forecasting 5, no. 1 (2023): 285–96. http://dx.doi.org/10.3390/forecast5010015.

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Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies am

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Sorjonen, Kimmo, Gustav Nilsonne, Michael Ingre, and Bo Melin. "Spurious correlations in research on ability tilt." Personality and Individual Differences 185 (February 2022): 111268. http://dx.doi.org/10.1016/j.paid.2021.111268.

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11

CUSHMAN, SAMUEL A., and ERIN L. LANDGUTH. "Spurious correlations and inference in landscape genetics." Molecular Ecology 19, no. 17 (2010): 3592–602. http://dx.doi.org/10.1111/j.1365-294x.2010.04656.x.

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12

Ye, Junyao, Zhen Li, Xi Tang, et al. "A Causal Learning Framework for Enhancing Robustness of Source Code Models." Proceedings of the ACM on Software Engineering 2, FSE (2025): 2641–64. https://doi.org/10.1145/3729387.

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Deep Learning (DL) models are useful for many software engineering tasks. However, these models are susceptible to adversarial attacks, partly because they learn spurious features that incur spurious correlations between these features and model predictions. In this paper, we tackle the problem with a novel causal learning framework, dubbed CausalCode, which leverages causal inference principles to mitigate spurious correlations. At a high level, CausalCode can be characterized as follows: (i) it uses causal data augmentation to generate intervention examples to disrupt spurious correlations;

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13

Li, Lei, Pierre Boué, and Michel Campillo. "Observation and explanation of spurious seismic signals emerging in teleseismic noise correlations." Solid Earth 11, no. 1 (2020): 173–84. http://dx.doi.org/10.5194/se-11-173-2020.

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Abstract. Deep body waves have been reconstructed from seismic noise correlations in recent studies. The authors note their great potential for deep-Earth imaging. In addition to the expected physical seismic phases, some spurious arrivals having no correspondence in earthquake seismograms are observed from the noise correlations. The origins of the noise-derived body waves have not been well understood. Traditionally, the reconstruction of seismic phases from inter-receiver noise correlations is attributed to the interference between waves from noise sources in the stationary-phase regions. T

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14

Dean, Thomas J., and Quang V. Cao. "Inherent Correlations Between Stand Biomass Variables Calculated from Tree Measurements." Forest Science 49, no. 2 (2003): 279–84. http://dx.doi.org/10.1093/forestscience/49.2.279.

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Abstract Correlating stand-level variables is an important component of forest production ecology; however, correlations among variables calculated with equations having common independent variables are potentially spurious. Monte Carlo simulation techniques were used to determine the inherent or null correlation coefficients among stand-level biomass variables calculated with published, individual-tree equations using loblolly pine (Pinus taeda L.) data. Null correlations of foliage mass/ha with branch mass/ha, stem mass/ha, and periodic annual increments of biomass were high with similar equ

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15

Freund, Jan A., and Alexander Cerquera. "How spurious correlations affect a correlation-based measure of spike timing reliability." Neurocomputing 81 (April 2012): 97–103. http://dx.doi.org/10.1016/j.neucom.2011.10.014.

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16

Tu, Lifu, Garima Lalwani, Spandana Gella, and He He. "An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models." Transactions of the Association for Computational Linguistics 8 (October 2020): 621–33. http://dx.doi.org/10.1162/tacl_a_00335.

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Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL w

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17

Coyne, Joseph O. C., Sophia Nimphius, Robert U. Newton, and G. Gregory Haff. "Does Mathematical Coupling Matter to the Acute to Chronic Workload Ratio? A Case Study From Elite Sport." International Journal of Sports Physiology and Performance 14, no. 10 (2019): 1447–54. http://dx.doi.org/10.1123/ijspp.2018-0874.

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Purpose: Criticisms of the acute to chronic workload ratio (ACWR) have been that the mathematical coupling inherent in the traditional calculation of the ACWR results in a spurious correlation. The purposes of this commentary are (1) to examine how mathematical coupling causes spurious correlations and (2) to use a case study from actual monitoring data to determine how mathematical coupling affects the ACWR. Methods: Training and competition workload (TL) data were obtained from international-level open-skill (basketball) and closed-skill (weightlifting) athletes before their respective quali

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18

Ninomiya, Yoshiyuki, Satoshi Kuriki, Toshihiko Shiroishi, and Toyoyuki Takada. "A modification of MaxT procedure using spurious correlations." Journal of Statistical Planning and Inference 214 (September 2021): 128–38. http://dx.doi.org/10.1016/j.jspi.2021.02.001.

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19

Lehrer, David, Janine Leschke, Stefan Lhachimi, Ana Vasiliu, and Brigitte Weiffen. "Brochures/Reviews/Reports: The Journal of Spurious Correlations." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 94, no. 1 (2007): 95–98. http://dx.doi.org/10.1177/075910630709400109.

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20

Aldrich, John. "Correlations Genuine and Spurious in Pearson and Yule." Statistical Science 10, no. 4 (1995): 364–76. http://dx.doi.org/10.1214/ss/1177009870.

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21

Calude, Cristian S., and Giuseppe Longo. "The Deluge of Spurious Correlations in Big Data." Foundations of Science 22, no. 3 (2016): 595–612. http://dx.doi.org/10.1007/s10699-016-9489-4.

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22

Bai, Yulong, Xiaoyan Ma, and Lin Ding. "A Fuzzy-Logic-Based Covariance Localization Method in Data Assimilation." Atmosphere 11, no. 10 (2020): 1055. http://dx.doi.org/10.3390/atmos11101055.

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In ensemble data assimilation systems, the impracticalities of full sampling and systematic error often lead to spurious correlations between two variables with low actual correlations. To solve these problems, researchers have previously proposed a covariance localization (CL) method, which mainly involves the Schur product between a state error covariance matrix and a distance-based correlation matrix. Although this CL method can reduce spurious correlations to a certain extent, observational data remain difficult to be used effectively, which results in unreasonable assimilation. In this st

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23

Zong, Daoming, Chaoyue Ding, Kaitao Chen, Yinsheng Li, and Shuaiyu Wang. "Counterfactual Debiasing for Physical Audiovisual Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 14 (2025): 15265–73. https://doi.org/10.1609/aaai.v39i14.33675.

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Physical commonsense is an essential aspect of human cognition, involving an intuitive understanding of the physical properties and interactions of everyday objects and materials. Though physical commonsense reasoning should inherently be a multisensory task, integrating both video and audio signals, existing physical audiovisual commonsense reasoning (PACR) models predominantly rely on visual information. This reliance leads to spurious correlations and undermines the models’ reasoning and generalization abilities. To counteract this, we introduce a model-agnostic Counterfactual Physical Audi

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24

Soares, Hélcio de Abreu, Raimundo Santos Moura, Vinícius Ponte Machado, Anselmo Paiva, Weslley Lima, and Rodrigo Veras. "The Detection of Spurious Correlations in Public Bidding and Contract Descriptions Using Explainable Artificial Intelligence and Unsupervised Learning." Electronics 14, no. 7 (2025): 1251. https://doi.org/10.3390/electronics14071251.

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Artificial Intelligence (AI) models, including deep learning and rule-based approaches, often function as black boxes, limiting transparency and increasing uncertainty in decisions. This study addresses spurious correlations, defined as associations between patterns and classes that do not reflect causal relationships, affecting AI models’ reliability and applicability. In Natural Language Processing (NLP), these correlations lead to inaccurate predictions, biases, and challenges in model generalization. We propose a method that employs Explainable Artificial Intelligence (XAI) techniques to d

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25

Jackson, Donald A., Harold H. Harvey, and Keith M. Somers. "Ratios in Aquatic Sciences: Statistical Shortcomings with Mean Depth and the Morphoedaphic Index." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 9 (1990): 1788–95. http://dx.doi.org/10.1139/f90-203.

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Researchers in aquatic sciences frequently employ empirically derived models to predict productivity, yield, and abundance of fish. We demonstrate that predictive models employing ratios of standardized biomass and lake morphometric variables are biased by spurious correlations due to mathematical transformations and the use of inappropriate null models. Our findings emphasise that studies incorporating ratios like mean depth or the morphoedaphic index require cautious interpretation. Future research should focus on more appropriate analytical approaches such as regression-based models like th

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26

Zanos, Theodoros P., Patrick J. Mineault, and Christopher C. Pack. "Removal of Spurious Correlations Between Spikes and Local Field Potentials." Journal of Neurophysiology 105, no. 1 (2011): 474–86. http://dx.doi.org/10.1152/jn.00642.2010.

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Single neurons carry out important sensory and motor functions related to the larger networks in which they are embedded. Understanding the relationships between single-neuron spiking and network activity is therefore of great importance and the latter can be readily estimated from low-frequency brain signals known as local field potentials (LFPs). In this work we examine a number of issues related to the estimation of spike and LFP signals. We show that spike trains and individual spikes contain power at the frequencies that are typically thought to be exclusively related to LFPs, such that s

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27

LIN, AIJING, and PENGJIAN SHANG. "MINIMIZING PERIODIC TRENDS BY APPLYING LAPLACE TRANSFORM." Fractals 19, no. 02 (2011): 203–11. http://dx.doi.org/10.1142/s0218348x11005245.

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Rescaled range analysis (R/S analysis), detrended fluctuation analysis (DFA) and detrended moving average (DMA) are widely-used methods for detection of long-range correlations in time series. Detrended cross-correlation analysis (DCCA) is a recently developed method to quantify the cross-correlations of two non-stationary time series. Another method for studying auto-correlations and cross-correlations was presented by Sergio Arianos and Anna Carbone in 2009. Recent studies have reported the susceptibility of this methods to periodic trends, which can result in spurious crossovers. In this pa

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28

Qian, Jiang, Yuval Kluger, Haiyuan Yu, and Mark Gerstein. "Identification and correction of spurious spatial correlations in microarray data." BioTechniques 35, no. 1 (2003): 42–48. http://dx.doi.org/10.2144/03351bm03.

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29

Leuridan, B., E. Weber, and M. Van Dyck. "The practical value of spurious correlations: selective versus manipulative policy." Analysis 68, no. 4 (2008): 298–303. http://dx.doi.org/10.1093/analys/68.4.298.

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30

Rodgers, Joseph L. "Birth order, SAT, and confluence: Spurious correlations and no causality." American Psychologist 43, no. 6 (1988): 476–77. http://dx.doi.org/10.1037/0003-066x.43.6.476.

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31

Meiser, Thorsten, and Miles Hewstone. "Contingency learning and stereotype formation: Illusory and spurious correlations revisited." European Review of Social Psychology 21, no. 1 (2010): 285–331. http://dx.doi.org/10.1080/10463283.2010.543308.

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32

Zhang, Lily H., and Rajesh Ranganath. "Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15305–12. http://dx.doi.org/10.1609/aaai.v37i12.26785.

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Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (OOD) detection of image inputs. However, as demonstrated in previous work, these methods struggle to detect OOD inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance OOD (SN-OOD) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for these

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33

Retailleau, Lise, Pierre Boué, Laurent Stehly, and Michel Campillo. "Locating Microseism Sources Using Spurious Arrivals in Intercontinental Noise Correlations." Journal of Geophysical Research: Solid Earth 122, no. 10 (2017): 8107–20. http://dx.doi.org/10.1002/2017jb014593.

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34

POLYAKOV, A. M. "SELF-TUNING FIELDS AND RESONANT CORRELATIONS IN 2d-GRAVITY." Modern Physics Letters A 06, no. 07 (1991): 635–44. http://dx.doi.org/10.1142/s0217732391000658.

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We show that minimal models coupled to quantum gravity have special type of resonant correlations defined by the free field representations and are explicitly computable. The key phenomena in these models are shown to be discontinuity of the number of degrees of freedom at exceptional values of momenta and incomplete decoupling of the spurious states.

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35

Chen, Yan, and Dean S. Oliver. "Localization of Ensemble-Based Control-Setting Updates for Production Optimization." SPE Journal 17, no. 01 (2011): 122–36. http://dx.doi.org/10.2118/125042-pa.

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Summary In the ensemble-based approach to production optimization (EnOpt), a steepest-ascent direction is computed from an ensemble of controls to iteratively improve a set of control settings. The method was shown to work well in maximizing field net present value (NPV) with an ensemble size of 104 on the Brugge SPE comparative test case for closed-loop optimization that had 84 controllable completion intervals (and 3,360 control variables), but performance of the method with smaller ensemble size or on larger problems might be difficult. Without regularization, the crosscovariance between co

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36

Andersson, Lars. "Loneliness and its Relationship with Misery." Psychological Reports 73, no. 2 (1993): 584–86. http://dx.doi.org/10.2466/pr0.1993.73.2.584.

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37

Calude, Cristian S., and Karl Svozil. "Spurious, Emergent Laws in Number Worlds." Philosophies 4, no. 2 (2019): 17. http://dx.doi.org/10.3390/philosophies4020017.

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We study some aspects of the emergence of lógos from xáos on a basal model of the universe using methods and techniques from algorithmic information and Ramsey theories. Thereby an intrinsic and unusual mixture of meaningful and spurious, emerging laws surfaces. The spurious, emergent laws abound, they can be found almost everywhere. In accord with the ancient Greek theogony one could say that lógos, the Gods and the laws of the universe, originate from “the void,” or from xáos, a picture which supports the unresolvable/irreducible lawless hypothesis. The analysis presented in this paper sugge

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38

Potter, Douglas M. "Comment on Mesoudi and O’Brien’s “The Cultural Transmission of Great Basin Projectile-Point Technology I: An Experimental Simulation”." American Antiquity 77, no. 2 (2012): 368–71. http://dx.doi.org/10.7183/0002-7316.77.2.368.

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AbstractMesoudi and O’Brien (2008) describe an experimental simulation of the cultural transmission of projectile-point technology. They base some of their conclusions on the results of tests of correlation. I explain why their use of correlation is improper, and hence that the validity of their conclusions is questionable at best. To support that assertion, I provide simulations to demonstrate that spurious correlations arise in the situations where the authors use correlation. I also describe analysis methods appropriate for their data.

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39

Zhao, Zixuan, Hengzhuang Li, Jinxuan Chen, Yang Li, and Jiayun Song. "Feature selection in text classification: Identifying spurious words with causal inference methods." Applied and Computational Engineering 6, no. 1 (2023): 1522–32. http://dx.doi.org/10.54254/2755-2721/6/20230334.

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As has been scrutinized by many, non-causal model may contain spurious correlations that act like shortcuts during the prediction phase, undermining cross-domain accuracy. This can be caused by biased training data that contains spurious words with neutral meanings yet can induce the model to predict wrongly. Based on this assumption, we propose a series of methods to detect these spurious words before feeding the model with the training data. We used advanced causal inference methods which are arising novas in recent studies, such as propensity score matching and inverse propensity score weig

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Pan, Yonghua, Zechao Li, Liyan Zhang, and Jinhui Tang. "Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (2022): 1–23. http://dx.doi.org/10.1145/3487042.

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Recently, many Visual Question Answering (VQA) models rely on the correlations between questions and answers yet neglect those between the visual information and the textual information. They would perform badly if the handled data distribute differently from the training data (i.e., out-of-distribution (OOD) data). Towards this end, we propose a two-stage unbiased VQA approach that addresses the unbiased issue from a causal perspective. In the causal inference stage, we mark the spurious correlation on the causal graph, explore the counterfactual causality, and devise a causal target based on

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41

Qiao, Yiran, Yu Yin, Chen Chen, and Jing Ma. "Certified Causal Defense with Generalizable Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20024–32. https://doi.org/10.1609/aaai.v39i19.34205.

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While machine learning models have proven effective across various scenarios, it is widely acknowledged that many models are vulnerable to adversarial attacks. Recently, numerous efforts have emerged in adversarial defense. Among them, certified defense is well known for its theoretical guarantees against arbitrary adversarial perturbations on input within a certain range. However, most existing works in this line struggle to generalize their certified robustness in other data domains with distribution shifts. This issue is rooted in the difficulty of eliminating the negative impact of spuriou

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42

Alvera-Azcárate, A., A. Barth, D. Sirjacobs, and J. M. Beckers. "Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF." Ocean Science 5, no. 4 (2009): 475–85. http://dx.doi.org/10.5194/os-5-475-2009.

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Abstract. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The

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43

Alvera-Azcárate, A., A. Barth, D. Sirjacobs, and J. M. Beckers. "Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF." Ocean Science Discussions 6, no. 2 (2009): 1547–68. http://dx.doi.org/10.5194/osd-6-1547-2009.

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Abstract. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The

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44

Páez, Antonio, Steven Farber, and David Wheeler. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships." Environment and Planning A: Economy and Space 43, no. 12 (2011): 2992–3010. http://dx.doi.org/10.1068/a44111.

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Large variability and correlations among the coefficients obtained from the method of geographically weighted regression (GWR) have been identified in previous research. This is an issue that poses a serious challenge for the utility of the method as a tool to investigate multivariate relationships. The objectives of this paper are to assess: (1) the ability of GWR to discriminate between a spatially constant processes and one with spatially varying relationships; and (2) to accurately retrieve spatially varying relationships. Extensive numerical experiments are used to investigate situations

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Bishop, Craig H., and Daniel Hodyss. "Adaptive Ensemble Covariance Localization in Ensemble 4D-VAR State Estimation." Monthly Weather Review 139, no. 4 (2011): 1241–55. http://dx.doi.org/10.1175/2010mwr3403.1.

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Abstract An adaptive ensemble covariance localization technique, previously used in “local” forms of the ensemble Kalman filter, is extended to a global ensemble four-dimensional variational data assimilation (4D-VAR) scheme. The purely adaptive part of the localization matrix considered is given by the element-wise square of the correlation matrix of a smoothed ensemble of streamfunction perturbations. It is found that these purely adaptive localization functions have spurious far-field correlations as large as 0.1 with a 128-member ensemble. To attenuate the spurious features of the purely a

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46

Zhang, Shengyu, Xusheng Feng, Wenyan Fan, et al. "Video-Audio Domain Generalization via Confounder Disentanglement." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 15322–30. http://dx.doi.org/10.1609/aaai.v37i12.26787.

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Existing video-audio understanding models are trained and evaluated in an intra-domain setting, facing performance degeneration in real-world applications where multiple domains and distribution shifts naturally exist. The key to video-audio domain generalization (VADG) lies in alleviating spurious correlations over multi-modal features. To achieve this goal, we resort to causal theory and attribute such correlation to confounders affecting both video-audio features and labels. We propose a DeVADG framework that conducts uni-modal and cross-modal deconfounding through back-door adjustment. DeV

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47

Wang, Zhao, and Aron Culotta. "Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14024–31. http://dx.doi.org/10.1609/aaai.v35i16.17651.

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Spurious correlations threaten the validity of statistical classifiers. While model accuracy may appear high when the test data is from the same distribution as the training data, it can quickly degrade when the test distribution changes. For example, it has been shown that classifiers perform poorly when humans make minor modifications to change the label of an example. One solution to increase model reliability and generalizability is to identify causal associations between features and classes. In this paper, we propose to train a robust text classifier by augmenting the training data with

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48

Sorjonen, Kimmo, and Bo Melin. "Spurious correlations in research on the effects of specific cognitive abilities." Personality and Individual Differences 187 (March 2022): 111417. http://dx.doi.org/10.1016/j.paid.2021.111417.

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49

Pawliuk, Micheal, and Michael Waddell. "Using Ramsey Theory to Measure Unavoidable Spurious Correlations in Big Data." Axioms 8, no. 1 (2019): 29. http://dx.doi.org/10.3390/axioms8010029.

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Given a dataset, we quantify the size of patterns that must always exist in the dataset. This is done formally through the lens of Ramsey theory of graphs, and a quantitative bound known as Goodman’s theorem. By combining statistical tools with Ramsey theory of graphs, we give a nuanced understanding of how far away a dataset is from correlated, and what qualifies as a meaningful pattern. This method is applicable to a wide range of datasets. As examples, we analyze two very different datasets. The first is a dataset of repeated voters ( n = 435 ) in the 1984 US congress, and we quantify how h

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Fellner, M.-C., G. Volberg, K. J. Mullinger, et al. "Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement." NeuroImage 133 (June 2016): 354–66. http://dx.doi.org/10.1016/j.neuroimage.2016.03.031.

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