The Bonferonni and Šidák Corrections for Multiple Comparisons

By Hervé Abdi. 2006

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Citations in the corpus

Findings of the 2017 Conference on Machine Translation (WMT17) (2017, methodology): “We test the significance of the results using randomisation tests (Yeh, 2000) with Bonferroni correction (Abdi, 2007).”
Direct 4D‐Var assimilation of all‐sky radiances. Part II: Assessment (2010, methodology): “Our approach would then be equivalent to making a Šidák correction for multiplicity (e.g. Abdi, 2007), assuming 21 independent tests in order to obtain a family-wide significance of 90%.”
3σ-Rule for Outlier Detection from the Viewpoint of Geodetic Adjustment (2013, background): “Because α ≪ 1 is typically chosen, the so-called Bonferroni equation (Abdi 2007) is derived from (1 − α) ≈ 1 − nα α ≈ nα.”
Development of Spodoptera frugiperda on different hosts and damage to reproductive structures in cotton (2010, methodology): “…analysis of variance (ANOVA) and Tukey’s studentized range test, with Bonferroni’s corrections for 0.05 of significance level (a = 0.05 ⁄ n, where ‘n’ represents the number of means in comparison to hold the error level equal or lower than 0.05; Abdi, 2007) using SAS software (SAS Institute, 2001).”
A survey of managed honey bee colony losses in the USA, fall 2009 to winter 2010 (2011, methodology): “When multiple comparisons were made, the α used to reject the null hypothesis was adjusted appropriately (Abdi, 2007).”
Metrics for Evaluation of Word-level Machine Translation Quality Estimation (2016, methodology): “We rank the systems according to all the metrics and compute the level of signiﬁcance for every 2 Systems that took part in the shared task are listed and described in (Bojar et al., 2015). pair of systems with randomisation tests (Yeh, 2000) with Bonferroni correction (Abdi, 2007).”
Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate (2018, methodology): “First, we highlight a hazard with the Brambor, Clark, and Golder procedure: The reported α level of CIs and hypothesis tests constructed using the procedure can be inaccurate because of a multiple comparison problem (Abdi, 2007; Sidak, 1967).”
Significance of changes in medium-range forecast scores (2016, methodology): “The normal way to control multiplicity is the Šidák correction (e.g. Abdi, 2007) which seeks to control the typeI error rate across a whole ‘family’ of statistical tests.”
Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary (2016, methodology): “Since the program investigates a potentially huge number of null hypotheses, additional filtration can be specified using the Bonferroni and Benjamini – Hochberg adjustments [16, 17] to correct for multiple comparisons.”
DALI shines a light on remote homologs: One hundred discoveries (2022, methodology): “…of matches ≥ N obs ð Þ : This test was repeated for each pair of sequence profiles and the resulting p -values were adjusted using the Bonferroni-correction method (Abdi, 2007) of controlling the FWER (the probability of at least one false positive when multiple comparisons are being tested).”
Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods (2014, methodology): “However, the Bonferroni procedure is mostly a too conservative adjustment method as most outcomes are interdependent (for example, all cause mortality and serious adverse events will often be positively correlated outcomes) [34].”
Worldwide Genetic Diversity for Mineral Element Concentrations in Rice Grain (2015, unknown): “Therefore significance thresholds (r ³ 0.310 for both flooded and unflooded conditions) were determined using Bonferroni’s adjustments (Abdi, 2007).”
Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization (2013, methodology): “If this hypothesis is rejected at the 95% confidence level, we then consider pairwise comparisons between the algorithms using the Wilcoxon-ranksum two-sided comparison procedure at the 95% confidence level, employing Šidák correction to reduce Type I errors [49].”
Seeing is Believing? Evidence from an Extension Network Experiment (2014, methodology): “To account for multiple hypothesis testing, we adjust our inferences for familywise error rates (Šidák, p-value=0.015; Bonferroni, p-value=0.014), following Abdi (2007).”
Multiple Outlier Detection: Hypothesis Tests versus Model Selection by Information Criteria (2016, background): “which is called the Bonferroni equation (Abdi 2007). Unfortunately, the test statistics [Eqs. (16a) and (16b)] and consequently the random events “reject a true H0 in test j” are statistically dependent. In the case ng = 1 (single outlier) the dependency is caused by Qvv being a nondiagonal matrix. ”