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The Interpretation of Interaction in Contingency Tables
Abstract
Summary The definition of second order interaction in a (2 × 2 × 2) table given by Bartlett is accepted, but it is shown by an example that the vanishing of this second order interaction does not necessarily justify the mechanical procedure of forming the three component 2 × 2 tables and testing each of these for significance by standard methods.
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“Indeed, the relationship between body composition and BMI represents a clear example of Simpson’s paradox (Simpson, 1951).”
From The degree to which the cultural ideal is internalized predicts judgments of male and female physical attractiveness (2022) · cites this paper as Simpson, 1951
In the citing paper
“The classical example demonstrating the obstacle of causal vocabulary is Simpson's paradox (Simpson, 1951) - a reversal phenomenon that earns its claim to fame only through a causal interpretation of the data (Pearl, 2000a, Chapter 6).”
From Causal inference in statistics: An overview (2009) · cites this paper as Simpson, 1951
In the citing paper
“Our work extends this to the causal domain, showing that models will endorse logical traps (like Simpson's Paradox (Simpson, 1951)) if pressed.”
From Diagnosing and Mitigating Sycophancy and Skepticism in LLM Causal Judgment (2026) · cites this paper as Simpson, 1951
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Two Statistical Paradoxes in the Interpretation of Group Differences
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“It shows up frequently when data are aggregated and is well known among statisticians (e.g., Blyth 1972; Rinott and Tamm 2003; Samuels 1993; Simpson 1951; Wainer 1986; Westbrooke 1998).”
BackgroundNam, J. (1998) · Biometrics · doi:10.2307/2534020 · Cited by 16
On Tests of independence in several dimensions
Lancaster, H. O. (1960) · Journal of the Australian Mathematical Society · doi:10.1017/s1446788700025593 · Cited by 16
“In the test of discrete distributions one has the choice of combinatorial and approximating tests such as x(2)- We shall not consider the combinatorial methods here and refer the reader to [1], [3], [8], [20], [21], [24], [27], [28], [29], [31], which cover the ground and give further references.”
MethodRidley, B. J., Cornelissen, P. L., Maalin, N., Mohamed, S., Kramer, R. S. S., McCarty, K., & Tovée, M. J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.980277 · Cited by 18
“Indeed, the relationship between body composition and BMI represents a clear example of Simpson’s paradox (Simpson, 1951).”
BackgroundThe Five-Factor Model of Personality: Consensus and Controversy
McCrae, R. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.013 · Cited by 20
Groves, V., Ridley, B. J., Cornelissen, P. L., Maalin, N., Mohamed, S., Kramer, R. S. S., McCarty, K., Tovée, M. J., & Cornelissen, K. K. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1116686 · Cited by 19
“This is called Simpson’s paradox (Simpson, 1951).”
Reeping, D. P., Lee, W. C., & London, J. (2023) · Journal of Engineering Education · doi:10.1002/jee.20530 · Cited by 15
“Simpson's Paradox is a classic phenomenon in statistics where trends are observed in different groups yet disappear or reverse when groups are aggregated (Simpson, 1951).”
BackgroundGanesh, M., Deo, S., & Devalkar, S. K. (2024) · Production and Operations Management · doi:10.1177/10591478241242119 · Cited by 17
Compound majority paradoxes and proportional representation
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The false linking of race and causality: lessons from standardized testing
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Simplicity and likelihood: An axiomatic approach
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Tulabandhula, T., & Rudin, C. D. (2014) · Big Data · doi:10.1089/big.2014.0018 · Cited by 15
A cross-scale view of N and P limitation using a Bayesian hierarchical model
Cha, Y., Alameddine, I., Qian, S. S., & Stow, C. A. (2016) · Limnology and Oceanography · doi:10.1002/lno.10375 · Cited by 16
Uthman, O. A., Ekström, A. M., & Moradi, T. (2016) · BMC Public Health · doi:10.1186/s12889-016-3637-1 · Cited by 15
“Furthermore, our findings reveal a striking example of Simpson’s paradox [39, 40], a situation where the association between two variables (e.”
BackgroundChi, H., Yang, L., Yang, W., Li, Y., Chen, Z., Huang, L., Chao, Y., Qiu, R., & Wang, S. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15122610 · Cited by 15
“The higher the value of the Simpson’s Paradox, the lower the diversity of the bacterial community.”
BackgroundSpatially Adaptive Colocalization Analysis in Dual-Color Fluorescence Microscopy
Wang, S., Arena, E. T., Becker, J. T., Bement, W. M., Sherer, N. M., Eliceiri, K. W., & Yuan, M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2909194 · Cited by 17
“colocalization in region A, which contradicts the expected biological outcomes of no colocalization see more discussion on Simpson’s paradox [41].”
BackgroundNormative and Descriptive Analyses of Simpson's Paradox in Decision Making
Curley, S. P., & Browne, G. J. (2001) · Organizational Behavior and Human Decision Processes · doi:10.1006/obhd.2000.2928 · Cited by 15
Can Two Wrongs Make a Right? Coin-Tossing Games and Parrondo’s Paradox
Percus, O. E., & Percus, J. K. (2002) · The Mathematical Intelligencer · doi:10.1007/bf03024736 · Cited by 15
On stratification, minimization and protection against types 1 and 2 error
Bracken, M. B. (2001) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(00)00286-9 · Cited by 15
An Applet for the Investigation of Simpson's Paradox
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(Quasi-)Experimental Studies in Industrial Settings
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Simpson's paradox resolved: A reply to Hintzman.
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Meta-analysis: A reflection on problems
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Statistics and death from meningococcal disease in children
Perera, R. (2006) · BMJ · doi:10.1136/bmj.332.7553.1297 · Cited by 15
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“3 Simpson [12] described the weak form of the paradox that now bears his name, although the phenomenon was known much earlier [13, 14; see also 3].”
BackgroundMethodResultInfluentialIdentifying Hydrocarbon Zones in Unconventional Formations by Discerning Simpson's Paradox
Zee, Y., Moore, W. R., Kaufman, P. S., Wang, Y., Gurpinar, O. M., Luneau, B., & Gomez, E. (2014) · SPE Western North American and Rocky Mountain Joint Meeting · doi:10.2118/169495-ms · Cited by 14
Steur, G., ter Steege, H., Verburg, R. W., Sabatier, D., Molino, J., Bánki, O., Castellanos, H., Stropp, J., Fonty, É., Ruysschaert, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-09786-6 · Cited by 17
“This reflects a known statistical phenomenon referred to as a ‘Simpson’s paradox’ 34 , in which a relationship appears in multiple distinct groups but disappears or reverses when the groups are combined.”
BackgroundAydın, O. (2023) · Neuropsychological Rehabilitation · doi:10.1080/09602011.2023.2181191 · Cited by 24
Schertler, M., Glumann, N. V., & Boehm, S. A. (2023) · Academy of Management Discoveries · doi:10.5465/amd.2022.0133 · Cited by 16
Hirano, S., Saitoh, E., Imoto, D., Ii, T., Tsunoda, T., & Otaka, Y. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-024-01370-5 · Cited by 14
“This phenomenon, where a trend appears in several subgroups but disappears or reverses when combined, is known as Simpson’s paradox [37].”
BackgroundWhen Causality Meets Fairness: A Survey
Makhlouf, K., Zhioua, S., & Catuscia Palamidessi, A. (2023) · Journal of Logical and Algebraic Methods in Programming · doi:10.1016/j.jlamp.2024.101000 · Cited by 14
Danchin, A. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.70014 · Cited by 13
“It has long been known that the conclusion drawn from data exploration depends heavily on how the data are grouped into classes, sometimes leading to opposite conclusions from the very same data (Simpson, 1951).”
BackgroundVisualizing Statistical Mix Effects and Simpson's Paradox
Armstrong, Z., & WATTENBERG, M. P. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346297 · Cited by 14
Contingency learning and stereotype formation: Illusory and spurious correlations revisited
Meiser, T., & Hewstone, M. (2010) · European Review of Social Psychology · doi:10.1080/10463283.2010.543308 · Cited by 14
“The stimulus distribution thereby forms an instance of Simpson’s Paradox (Simpson, 1951), where a spurious correlation between group membership and outcome is induced if the context factor of task difficulty is not taken into account.”
BackgroundInfluentialDeformation-induced transitional myofibroblasts contribute to compensatory lung growth
Bennett, R. D., Ysasi, A. B., Wagner, W. L., Valenzuela, C. D., Tsuda, A., Pyne, S., Li, S., Grimsby, J., Pokharel, P., Livak, K. J., et al. (2016) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00383.2016 · Cited by 14
“Single-cell qPCR allowed us to avoid “Simpson’s paradox” (32); that is, the statistical observation that findings attributable to a particular subset (e.”
Backgroundvan den Bergh, J. C. J. M. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108564922.009 · Cited by 13
Validating spatio-temporal environmental machine learning models: Simpson’s paradox and data splits
Boser, A. (2024) · Environmental Research Communications · doi:10.1088/2515-7620/ad2e44 · Cited by 12
Müller, R., Thoß, M., Ullrich, J., Seitz, S., & Knoll, C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2323263 · Cited by 15
“This is a well-known statistical phenomenon called Simpson’s paradox (Simpson, 1951).”
Concepts and a Case Study for a Flexible Class of Graphical Markov Models
Wermuth, N., & Cox, D. R. (2013) · Springer eBooks · doi:10.1007/978-3-642-35494-6_20 · Cited by 13
“Singleton transitivity is satisfied in binary distributions; see Simpson (1951).”
BackgroundTseranidis, S., Theodoridis, L., Loukogeorgaki, E., & Angelides, D. C. (2016) · Marine Structures · doi:10.1016/j.marstruc.2016.09.003 · Cited by 13
Bokhove, C., Miyazaki, M., Komatsu, K., Chino, K., Leung, A. Y. L., & Mok, I. A. C. (2019) · Frontiers in Education · doi:10.3389/feduc.2019.00063 · Cited by 13
“This emphasizes the strength of multilevel models, as this seems to indicate a form of Simpson’s paradox, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined (Simpson, 1951).”
BackgroundUpdated IQ and Well-Being Scores for the 50 U.S. States
Pesta, B. J. (2022) · Journal of Intelligence · doi:10.3390/jintelligence10010015 · Cited by 13
“A related potential limitation is Simpson’s paradox (Simpson 1951), wherein aggregating data sometimes leads to vastly different results than those found without aggregation.”
BackgroundA generalized bivariate Bernoulli model with covariate dependence
Islam, M. A., Alzaid, A. A., Chowdhury, R. I., & Sultan, K. S. (2013) · Journal of Applied Statistics · doi:10.1080/02664763.2013.780156 · Cited by 13
“Wakefield [38] summarizes the limitation of the marginal models with specific reference to the well-known Simpson’s paradox [36].”
MethodPearl, J. (2016) · Journal of Causal Inference · doi:10.1515/jci-2016-0005 · Cited by 13
“, “A person prefers finding f to finding g”) than the STP is plainly false as we know only too well from Simpson’s paradox (Simpson, 1951; Pearl, 2014).”
Causation and Statistical Inference
Glymour, C. (2010) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199279739.003.0024 · Cited by 13
Discovering markers of healthy aging: a prospective study in a Danish male birth cohort
Zarnani, K., Nichols, T. E., Alfaro‐Almagro, F., Fagerlund, B., Lauritzen, M., Rostrup, E., & Smith, S. M. (2019) · Aging · doi:10.18632/aging.102151 · Cited by 13
Test-driven development with mutation testing – an experimental study
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“However, due to the small size of samples (eight teams, each of three or two students), randomization may lead to the well-known Simpson Paradox (Simpson, 1951).”
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Multilevel Modeling in Classical Twin and Modern Molecular Behavior Genetics
Hunter, M. D. (2021) · Behavior Genetics · doi:10.1007/s10519-021-10045-z · Cited by 11
“It is (5) Cov � p1 p2 � = �√ 1 − r 0 0 √ 1 − r �� a2 0 0 a2 ��√ 1 − r 0 0 √ 1 − r � + �√ r√ r �� a2 ��√r√ r � + � 1 1 �� c2 �� 1 1 � + � 1 0 0 1 �� e2 0 0 e2 �� 1 0 0 1 � well-known that within-family effects need not be replicated at the between-family level (Robinson 1950; Simpson 1951).”
Burch, G. S. J., & Anderson, N. R. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511596544.046 · Cited by 13
Best Practices in Detecting Bias in Nonverbal Tests
Maller, S. J. (2003) · Springer eBooks · doi:10.1007/978-1-4615-0153-4_2 · Cited by 13
How to detect reasoning-remembering dependence (and how not to)
CHAPMAN, M. (1992) · Developmental Review · doi:10.1016/0273-2297(92)90008-p · Cited by 13
Impact of anti-discrimination laws on credit scoring
Andreeva, G., Ansell, J., & Crook, J. (2004) · Journal of Financial Services Marketing · doi:10.1057/palgrave.fsm.4770138 · Cited by 13
A Note on Collapsibility in DAG Models of Contingency Tables
Kim, S.-H., & Kim, S. (2006) · Scandinavian Journal of Statistics · doi:10.1111/j.1467-9469.2005.00490.x · Cited by 13
“Discussion of this issue of collapsibility dates back to the Yule-Simpson paradox (Simpson, 1951) and is continued by many investigators including Darroch (1962), Lewis (1962), Birch (1963) and Plackett (1969), to name only the first few in this line of work.”
BackgroundFitzmaurice, G. M. (2003) · Nutrition · doi:10.1016/s0899-9007(02)00959-0 · Cited by 13
Palindromic Bernoulli distributions
Marchetti, G. M., & Wermuth, N. (2016) · Electronic Journal of Statistics · doi:10.1214/16-ejs1175 · Cited by 13
“The property has been studied for binary variables by Simpson (1951, sec.”
The hazards of using active clinic patients as a source of subjects for clinical studies
Detsky, A. S., O’Rourke, K., Corey, P., Johnston, N., Fenton, S., & Jeejeebhoy, K. N. (1988) · Journal of General Internal Medicine · doi:10.1007/bf02596342 · Cited by 13
Analysis of linkage and association for diseases of genetic aetiology
Sturt, E. (1984) · Statistics in Medicine · doi:10.1002/sim.4780030108 · Cited by 13
Rubin, D. B. (2000) · Springer eBooks · doi:10.1007/978-1-4612-1216-4_16 · Cited by 13
Log-Linear Modelling of Geographical Contingency Tables
Fingleton, B. (1981) · Environment and Planning A Economy and Space · doi:10.1068/a131539 · Cited by 13
Frey, R., Duncan, S., & Weber, E. U. (2020) · doi:10.31234/osf.io/yjwr9 · Cited by 13
The ubiquity of the Simpson’s Paradox
Selvitella, A. (2017) · Journal of Statistical Distributions and Applications · doi:10.1186/s40488-017-0056-5 · Cited by 16
“In his paper (Simpson 1951), Simpson considered a 2 × 2 × 2 contingency table with attributes A, B, and C and illustrated the paradox using a heuristic example of clinic patients, divided into a Treatment Group and in a No-Treatment group.”
BackgroundMethodCausal Artificial Intelligence in Legal Language Processing: A Systematic Review
Tritto, P. P., & Ponce-Espinosa, H. (2025) · Entropy · doi:10.3390/e27040351 · Cited by 23
“[18] This exemplifies Simpson’s paradox [19], where a correlation valid from a mathematical perspective becomes discriminatory from a legal standpoint.”
Ethnic differences and inequities in paediatric healthcare utilisation in the UK: a scoping review
Zhang, C. X., QUIGLEY, M. A., Bankhead, C. R., Bentley, T., Otasowie, C., & Carson, C. (2022) · Archives of Disease in Childhood · doi:10.1136/archdischild-2022-324577 · Cited by 12
“…of conflicting findings from the included studies depending on whether ethnicity was studied at an aggregate or disaggregated level also suggests that Simpson’s paradox may be at play, 33 and reinforces the need to understand underlying pathways to ethnic inequities at a more granular level.”
Chong, L. S., Lin, B., & Gordis, E. B. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108496 · Cited by 13
Cross-linguistic conditions on word length
Wichmann, S., & Holman, E. W. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281041 · Cited by 12
“This kind of mismatch between within-group and overall relationships is known as Simpson’s paradox, after [35].”
BackgroundEngagement and Anonymity in Online Computer Science Course Forums
Sharma, M., McTavish, H., Peng, Z., Shah, A., Agarwal, V., Sih, C., Hogan, E., Molina, I. V., Raj, A. G. S., & Vaccaro, K. (2023) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3568813.3600121 · Cited by 12
“This is a prime illustration of Simpson’s paradox [67], and a motivation to be as comprehensive in recording identity and contextual information as possible, as argued in prior work in the computing education community [46].”
MethodAcuff, S. F., Oddo, L. E., Johansen, A. N., & Strickland, J. C. (2024) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2024.173802 · Cited by 11
Stackhouse, M. R. D., Rickley, M., Liu, Y., & Taras, V. (2024) · Personality and Individual Differences · doi:10.1016/j.paid.2024.112795 · Cited by 12
“This uncertainty arises from the methodologies underlying both sets of studies, which may have adopted oversimplified or imperfect assumptions about within-nation heterogeneity, potentially leading to aggregation bias or susceptibility to Simpson's Paradox.”
BackgroundLongitudinal Sex-at-Birth and Age Analyses of Cortical Structure in the ABCD Study
Marshall, A. T., Adise, S., Kan, E., & Sowell, E. R. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1091-24.2025 · Cited by 13
Towards a personalized happiness approach to capturing change in satisfaction
Beck, E. D., Cheung, F., Thapa, S., & Jackson, J. J. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02171-z · Cited by 25
Tests of ignoring and eliminating in nonsymmetric correspondence analysis
Takane, Y., & Jung, S. (2009) · Advances in Data Analysis and Classification · doi:10.1007/s11634-009-0054-7 · Cited by 12
“In particular, the analysis sheds further light on an old problem known as Simpson’s paradox in statistics (Simpson 1951; Yule 1903).”
MethodEmergence of income inequality: Origin, distribution and possible policies
Tian, S., & Liu, Z. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.122767 · Cited by 12
DeRamus, T. P., Wu, L., Qi, S., Iraji, A., Silva, R. F., Du, Y., Pearlson, G. D., Mayer, A. R., Bustillo, J., Stromberg, S. F., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103056 · Cited by 12
“Second, to probe the data for the presence of Simpson ’ s (Simpson, 1951) or Lord ’ s (Lord, 1967; Lord, 1969) paradoxes within the data, which have been documented to generate problematic interpretations when not addressed (Kievit et al., 2013; Roberts et al., 2016; Teuscher, 2022).”
Risk-taking in social Parrondo’s games can lead to Simpson’s paradox
Lai, J. W., & Cheong, K. H. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.111911 · Cited by 12
Nie, R., Liu, Q., & Wang, X. (2022) · Journal of Oncology · doi:10.1155/2022/6964550 · Cited by 12
“These results reflect Simpson’s paradox [34], an uninformative statistical error.”
ResultMistraletti, G., Lancioni, A., Bassi, G., Nespoli, F., Umbrello, M., Salini, S., Zangrillo, A., Pappalardo, F., Scandroglio, A. M., Foti, G., et al. (2022) · Resuscitation · doi:10.1016/j.resuscitation.2022.11.025 · Cited by 12
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Cao, D., Enouen, J., Wang, Y., Song, X., Meng, C., Niu, H., & Liu, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25844 · Cited by 12
Multiscale selection in spatially structured populations
Doekes, H. M., & Hermsen, R. (2024) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2023.2559 · Cited by 11
“Counter-intuitively, the e ect of natural selection can thus be to reduce the mean of in every 54 local environment, while driving it up globally; a spatial Simpson’s paradox [23, 28].”
Tran, P., & Waller, L. A. (2014) · Environmental Research · doi:10.1016/j.envres.2014.08.041 · Cited by 12
Maximal Simpson-Disaggregations of 2 × 2 Tables
Zidek, J. (1984) · Biometrika · doi:10.1093/biomet/71.1.187 · Cited by 12
Likelihood Ratio Tests With Three-Way Tables
Cheng, P. E., Liou, M., & Aston, J. A. D. (2010) · Journal of the American Statistical Association · doi:10.1198/jasa.2010.tm09061 · Cited by 12
“Norton (1945) extended the discussion to finite K tables, and Simpson (1951) supplied interpretations of various interactions.”
BackgroundRajpurohit, S. S., Sinha, R. K., Sen, P., & Singh, S. (2018) · Arabian Journal of Geosciences · doi:10.1007/s12517-018-3913-8 · Cited by 12
Cho, E.-H., Eom, H.-J., & Jang, S. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17113927 · Cited by 12
Mikkelsen, N. J., Young, N. T., & Caballero, M. D. (2021) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.17.010109 · Cited by 12
Pachur, T., Schooler, L. J., & Stevens, J. R. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388435.003.0007 · Cited by 12
Simple Heuristics in a Social Game
Hertwig, R., Fischbacher, U., & Bruhin, A. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388435.003.0002 · Cited by 12
The ABC of pharmaceutical trial design: some basic principles
Julious, S., & Zariffa, N. (2002) · Pharmaceutical Statistics · doi:10.1002/pst.6 · Cited by 11
“This reversal effect is known as Simpson’s paradox [13,14].”
BackgroundCONFOUNDING, HOMOGENEITY AND COLLAPSIBILITY FOR CAUSAL EFFECTS IN EPIDEMIOLOGIC STUDIES
Geng, Z., Guo, J., Lau, T. S., & Fung, W. (2001) · Cited by 12
Some collapsibility results for n-dimensional contingency tables
Vellaisamy, P., & Vijay, V. (2007) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-006-0058-4 · Cited by 12
“This paradox, due to Simpson (1951) , known as Simpson’s paradox.”
Backgroundvan den Bergh, J. C. J. M. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108564922.014 · Cited by 12
Wainer, H. (2002) · CHANCE · doi:10.1080/09332480.2002.10554815 · Cited by 12
The Is and Ought of Sharing: The Equality Heuristic Across the Lifespan
Keller, M., Gummerum, M., Canz, T., Gigerenzer, G., & Takezawa, M. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388435.003.0006 · Cited by 12
Asch, D. A., & Armstrong, K. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.09.3336 · Cited by 12
Yule's Association Paradox and Ignored Stratum Heterogeneity in Capture—Recapture Studies
Kadane, J. B., Meyer, M. M., & Tukey, J. W. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474190 · Cited by 12
“Wachter and Speed (1991) questioned the methodology of poststrata, alleging that if within a poststratum there is a group of people resembling "wily trout," relatively difficult to count in the census and relatively difficult to find in the PBS, then a homogeneity assumption within the poststrata would be violated, in…”
MethodLog-linear models, screening and regional industrial surveys
UPTON, G. J. G. (1981) · Regional Studies · doi:10.1080/09595238100185041 · Cited by 12
The Drug Titration Paradox is Simpson’s Paradox
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Hirayama, passive smoking and lung cancer: 30 years on and the numbers still don’t lie
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Metadata
- Authors
- Simpson, E. H.
- Corresponding author
- Simpson, E. H.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1951-07-01
- Volume
- 13
- Issue
- 2
- Pages
- 238-241
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2905472553
- Cited by
- 2,301
- References
- 6
- Citations by year
- 2026: 92 · 2025: 136 · 2024: 122 · 2023: 113 · 2022: 116 · 2021: 121 · 2020: 165 · 2019: 101 · 2018: 94 · 2017: 78 · 2016: 87 · 2015: 84 · 2014: 75 · 2013: 57 · 2012: 109
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- second-order interactions, three-way contingency tables, marginal homogeneity, interaction testing, categorical data analysis
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