Cited by 2,785Probability and Statistical ResearchStatistical Distribution Estimation and ApplicationsGame Theory and Voting Systems

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A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings

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Friedman, Milton·1940·The Annals of Mathematical Statistics

DOI 10.1214/aoms/1177731944

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The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005
Self-citation
Statistical Tables for Biological, Agricultural and Medical Research.
Reed, L. J., Fisher, R. A., & Yates, F. (1944) · Journal of the American Statistical Association · doi:10.2307/2280633 · Cited by 3,012
The Problem of $m$ Rankings
Kendall, M. G., & Smith, B. B. (1939) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732186 · Cited by 992
The Correlation Ratio for Ranked Data
Wallis, W. A. (1939) · Journal of the American Statistical Association · doi:10.1080/01621459.1939.10503552 · Cited by 58

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“The Friedman statistic is expressed [45] as”
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(2021) · doi:10.1186/s40537-020-00398-3 · Cited by 197
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Yudes, C., Macizo, P., & Bajo, M. T. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00309 · Cited by 204
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Stojanova, D., Panov, P., Gjorgjioski, V., Kobler, A., & Džeroski, S. (2010) · Ecological Informatics · doi:10.1016/j.ecoinf.2010.03.004 · Cited by 183
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“• Friedman test [23].”
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“The Friedman test [62,63] and the two-way variance analysis, by ranks, were performed for the determination of the difference significance between the novel CFAEE and the alternative methods used for comparison.”
Method
Development of the Review Quality Instrument (RQI) for Assessing Peer Reviews of Manuscripts
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Alleviating naive Bayes attribute independence assumption by attribute weighting
Zaidi, N. A., Cerquides, J., Carman, M., & Webb, G. I. (2013) · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · doi:10.13039/501100000923 · Cited by 169
Non-parametric statistics for psychological research.
Moses, L. E. (1952) · Psychological Bulletin · doi:10.1037/h0056813 · Cited by 169
Multi-label feature selection based on neighborhood mutual information
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“Moreover, in terms of statistical testing, the Friedman non-parametric multiple groups test [63] with a significance level of α = 0.”
Method
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Fernández, J. C. F., Martinez, F. J., Hervás‐Martínez, C., & Gutiérrez, P. A. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2041468 · Cited by 162
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“The magnitude of results’ variances between the HAOA and every other method implemented in CEC2017 simulations can be determined from a Friedman test [134,135] and two-way ranks variance analysis.”
Method
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QoS-aware cloud service composition using eagle strategy
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“We used Friedman’s test [53] for the repeated measures to both ACCs and AUCs.”
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Authors: Friedman, M.
Corresponding author: Friedman, Milton
Venue: The Annals of Mathematical Statistics
Publisher: Institute of Mathematical Statistics
Published: 1940-03
Volume: 11
Issue: 1
Pages: 86-92
ISSN: 0003-4851 (ISSN-L)
DOI: 10.1214/aoms/1177731944
OpenAlex: W2016944307
Cited by: 2,785
References: 4
Citations by year: 2026: 191 · 2025: 256 · 2024: 244 · 2023: 253 · 2022: 264 · 2021: 231 · 2020: 209 · 2019: 173 · 2018: 133 · 2017: 106 · 2016: 132 · 2015: 93 · 2014: 82 · 2013: 72 · 2012: 67
FWCI: 5.07
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: The Annals of Mathematical Statistics (publishedVersion)
Project Euclid (Cornell University) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
Keywords: tests of significance, rank tests, comparison of statistical tests

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A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings

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