Multiple Comparisons among Means

By O. Dunn. 1961

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Citations in the corpus

Algorithmic stability for adaptive data analysis (2015, methodology): “This problem persists despite decades of research by statisticians on methods for preventing false discovery, such as the widely used Bonferroni Correction [Bon36, Dun61] and the Benjamini-Hochberg Procedure [BH95].”
Interactive fingerprinting codes and the hardness of preventing false discovery (2014, background): “Using a connection to differential privacy Dwork et al. (2006), Dwork et al. (2015) showed that there is a computationally efficient oracle that accurately answers Ω̃(n2) many adaptively chosen queries. They also showed that there is an exponential-time oracle that can answer exponentially in n many”
An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions (2017, methodology): “To control the family-wise error rate (FWER), a set of post hoc procedures, including the Bonferroni–Dunn procedure (Dunn 1961), the Holm procedure (Holm 1979), the Holland procedure (Holland and Copenhaver 1987), the Finner procedure (Finner 1993), theHochberg procedure (Hochberg 1988), theHommel p”
Teaching Students with Moderate Intellectual Disabilities to Read: An Experimental Examination of a Comprehensive Reading Intervention (2010, unknown): “This adjustment was made to help control for Type I error (Dunn, 1961).”
Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke (2011, unknown): “The right column shows the difference in coupling strength between patients and controls at different sessions (p b 0.05 corrected for multiple comparisons; Dunn, 1961).”
Soil extracellular enzyme activities correspond with abiotic factors more than fungal community composition (2013, methodology): “All P values were adjusted using the Bonferroni correction (Dunn 1961) to account for multiple comparisons.”
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms (2011, methodology): “The Bonferroni–Dunn procedure (Dunn–Sidak approximation) [37]: this adjusts the value of α in a single step by dividing it by the number of comparisons performed, ( k − 1 ) .”
Nonparametric Pairwise Multiple Comparisons in Independent Groups using Dunn's Test (2015, methodology): “ma(method) specifies the method of adjustment used for multiple comparisons and takes one of the following values: none, bonferroni, sidak, holm, hs, or bh. The default is ma(none). These methods perform as follows: none specifies that no adjustment for multiple comparisons be made. bonferroni speci”
Best (but oft-forgotten) practices: the multiple problems of multiplicity-whether and how to correct for many statistical tests. (2015, background): “It was named after the Italian mathematician Carlo Emilio Bonferroni and probably was first introduced into the statistical world by Olive Dunn (6, 7).”
Trial Sequential Analysis in systematic reviews with meta-analysis (2017, methodology): “This procedure is identical to the Bonferroni procedure for interim analyses and a final analysis of a single trial [30].”
Gender identity and lexical variation in social media (2012, methodology): “In this methodology, accurate predictions justify a post hoc analysis to identify the words that are the most effective predictors; these words are then assembled into groups.”
Making choices in Russian: pros and cons of statistical methods for rival forms (2013, methodology): “577 Gelöscht: the table This can be corrected by applying the Bonferroni correction (Dunn 1961), 579 which works as follows for the present example.”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, unknown): “%) by applying a Bonferroni correction (Dunn, 1961).”
Structured Comparative Analysis of Systems Logs to Diagnose Performance Problems (2012, methodology): “05 to reject the null hypothesis and assess significance, adjusting for multiple comparisons with a Bonferroni correction [11] based on the total number of features evaluated.”