Significance of Bonferroni correction
Bonferroni correction is a statistical method used to adjust significance levels when multiple comparisons are made. It aims to reduce the risk of false positives by accounting for the increased chance of finding a significant result by chance alone. This adjustment is crucial in various fields, including science, psychiatry, and health sciences, to ensure the accuracy and reliability of study results when comparing multiple groups or variables.
Synonyms: Bonferroni adjustment, Bonferroni method
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The concept of Bonferroni correction in scientific sources
Bonferroni correction is a statistical method used to adjust p-values or significance levels in studies with multiple comparisons. It aims to reduce false positives by correcting for multiple comparisons, thereby ensuring the validity and reliability of the study's results, especially in ANOVA analyses.
From: International Journal of Pharmacology
(1) This post hoc method was applied for subgroup evaluation following the ANOVA test.[1] (2) These adjustments were used in conjunction with ANOVA to correct for multiple comparisons, ensuring the reliability of statistical significance.[2] (3) A statistical method applied during GO term enrichment analysis to adjust for multiple comparisons and reduce the risk of false positives.[3] (4) Bonferroni correction is a statistical method used to adjust significance levels when performing multiple comparisons, to reduce the risk of false positives.[4] (5) Bonferroni correction was considered for use with the Mann-Whitney U-test to adjust for multiple comparisons.[5]
From: The Malaysian Journal of Medical Sciences
(1) This is a statistical method applied to adjust for multiple comparisons, ensuring the reliability of the study's results by correcting for potential type I errors.[6] (2) The Bonferroni correction is a statistical method used to adjust for multiple comparisons, helping to avoid false positive results when analyzing several groups.[7]
From: South African Journal of Physiotherapy
(1) This is a statistical method used to adjust the significance level when multiple comparisons are made, to reduce the chance of false positive results in the study.[8] (2) This is a statistical adjustment used to account for multiple comparisons, ensuring that the results are accurate and reliable.[9]
From: International Journal of Pharmacology
(1) A statistical method used to adjust significance levels when performing multiple comparisons.[10] (2) Bonferroni correction is a statistical method used with ANOVA to adjust for multiple comparisons, helping to maintain the overall significance level.[11] (3) A method used to correct for multiple comparisons in statistical analysis.[12] (4) A statistical method used to adjust significance levels when performing multiple comparisons, to reduce the risk of false positives.[13] (5) These adjustments were applied to the ANOVA results to correct for multiple comparisons, ensuring statistical validity.[14]
From: South African Journal of Psychiatry
(1) This is a statistical method used to adjust p-values, comparing the p-values given in the table with 0.025 or 0.0167.[15] (2) This is a statistical method used to adjust for multiple comparisons in order to reduce the chance of false positives, and it was applied in this study.[16]