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Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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McNemar's Test

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Related Experiment Video

Updated: Jun 2, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Permutational Multiple Testing Adjustments With Multivariate Multiple Group Data.

James F Troendle¹, Peter H Westfall

¹Biostatistics and Bioinformatics Branch of the Division of Epidemiology, Statistics, and Prevention Research of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH/DHHS.

Journal of Statistical Planning and Inference

|April 26, 2011

Summary

This summary is machine-generated.

This study introduces a new method for handling multiple comparisons across various group collections and outcomes. The approach simplifies complex statistical testing while maintaining accuracy, as shown through simulations and a clinical trial example.

Related Experiment Videos

Last Updated: Jun 2, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

Statistics
Biostatistics
Clinical Trials

Background:

The multiple comparison problem arises when analyzing multiple outcomes across different groups.
Existing methods often require distinct permutational approaches for different hypothesis types.

Purpose of the Study:

To develop a unified and simplified method for addressing multiple comparison problems in a multi-group setting.
To enable easier performance of statistical testing procedures for complex hypotheses.

Main Methods:

Utilizing a multivariate condition to apply closure across all hypotheses.
Employing Boole's inequality with permutation distributions for intersection hypotheses.
Developing shortcut tests for efficient procedure implementation.

Main Results:

Demonstrated the possibility of using closure over all hypotheses under specific multivariate conditions.
Developed shortcut tests that simplify the testing procedure.
Simulation studies showed competitive error rates and power compared to existing methods, even with correlated data.

Conclusions:

The proposed method offers a practical solution for complex multiple comparison scenarios.
The new approach is effective and efficient, as validated by simulations and a clinical trial example involving adverse events.