A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong

By A. Vargha, H. Delaney. 2000

1,662 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b1e8bcbf26cc43084397612f418fa5ce2efd52a1

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In Statistics and Probability

Citations in the corpus

Directed Greybox Fuzzing (2017, background): “(Â12) is a non-parametric measure of effect size [39] and is also the recommended standard measure for the evaluation of randomized algorithms [1].”
Engineering stress culture: Relationships among mental health, engineering identity, and sense of inclusion (2021, methodology): “We interpret Mann–Whitney U test effect sizes as Cliff's delta of 0.11 to be a small effect size, 0.28 a medium effect size, and 0.43 a large effect size (Vargha & Delaney, 2000).”
An improved effect size for single-case research: nonoverlap of all pairs. (2009, methodology): “CL was critiqued and extended by Vargha and Delaney (2000) to cover ordinal and discrete data, and they renamed the new index the “Measure of Stochastic Superiority.””
Effect size estimates: current use, calculations, and interpretation. (2012, neutral): “This concept has been elaborated and extended by Vargha and Delaney (2000) to include all types of ordinal and interval data.”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, methodology): “05, and the Vargha-Delaney (Â12) statistic [50] to measure the effect size.”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, methodology): “643 The Vargha and Delaney’ŝ A12 statistic is a non-parametric effect size measure [116, 55].”
A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, background): “To see how confidence intervals can be calculated, please see [25] and [60].”
The plastic surgery hypothesis (2014, methodology): “To assess whether the effect size is worthy of interest we employed a non-parametric effect size measure, namely the Vargha and Delaney’s A12 statistic [37].”
Hawkeye: Towards a Desired Directed Grey-box Fuzzer (2018, methodology): “In AFLGo’s paper, the A 12 metric [42] is used to show the possibility that one fuzzer is better than the other according to all the runs.”
Mutation-Driven Generation of Unit Tests and Oracles (2010, methodology): “To quantify the improvement in a standardized way, we used the Vargha-Delaney Â12 effect size [47].”
Deep Learning Based Vulnerability Detection: Are We There Yet? (2020, neutral): “al amount. To assert statistically sound comparisons, following previous approaches [67]–[71], we use a non-parametric bootstrap hypothesis test [72] in conjunction with the A12 effect size test [73]–[75]. We distinguish results from different experiments if both signiﬁcance test and effect size tes”
The strength of random search on automated program repair (2014, methodology): “Then, we use Mann-Whitney-Wilcoxon test [41] and A-test [36], the two nonparametric approaches, to qualitatively and quantitatively analyze the difference significance.”
Whole Test Suite Generation (2013, methodology): “To quantify the improvement in a standardized way, we used the Vargha-Delaney Â12 effect size [44].”