1961
DOI: 10.1080/01621459.1961.10482090
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Multiple Comparisons among Means

Abstract: Methods for constructing simultaneous confidence intervals for all possible linear contrasts among several means of normally distributed variables have been given by Scheff6 and Tukey. In this paper the possibility is considered of picking in advance a number (say m) of linear contrasts among k means, and then estimating these m linear contrasts by confidence intervals based on a Student t statistic, in such a way that the overall confidence level for the m intervals is greater than or equal to a preassigned v… Show more

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Cited by 3,408 publications
(1,086 citation statements)
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References 2 publications
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“…Analyses were conducted using the R package MuMIn version 1.15.6 (Barton, 2016). We tested the significance of the differences in mean ω among the twelve predictors using a Kruskal–Wallis test (Hollander & Wolfe, 1973), followed by a post hoc Dunn test (Dunn, 1961) to check the significance of pairwise differences, using the R package “ dunn.test” version 1.3.5.…”
Section: Methodsmentioning
confidence: 99%
“…Analyses were conducted using the R package MuMIn version 1.15.6 (Barton, 2016). We tested the significance of the differences in mean ω among the twelve predictors using a Kruskal–Wallis test (Hollander & Wolfe, 1973), followed by a post hoc Dunn test (Dunn, 1961) to check the significance of pairwise differences, using the R package “ dunn.test” version 1.3.5.…”
Section: Methodsmentioning
confidence: 99%
“…We used the Bonferroni method to correct for familywise error (false positives) when doing multiple comparisons (Dunn 1961). Using the traditional value of 0.05 to define the significance threshold, we divided 0.05 by the number of comparisons, 13, to redefine the threshold for significance for this study.…”
Section: English Language Competence Study Methodsmentioning
confidence: 99%
“…There were significant differences in oxidation rates for all columns at the different temperatures (p < 0.01). Post hoc T-tests with the Bonferonni correction were used to compare oxidation rates at the various temperatures in pairs (Dunn 1961). Oxidation rates for all columns were statistically higher at 31°C than at 22°C; oxidation rates at 22°C were statistically higher than at 4°C.…”
Section: Statisticsmentioning
confidence: 99%
“…P-values are the estimated probability that a null hypothesis should be rejected, where the null hypothesis is that data sets are not significantly different (Montgomery and Runger 2014 When multiple comparisons are made using T-tests, a "Bonferroni correction" is applied to reduce compounding of Type I errors (Dunn 1961). The Bonferroni correction divides alpha by the number of post-hoc comparisons made.…”
Section: Ii) Data Correlationmentioning
confidence: 99%