Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
Multiple Comparison Tests01:13

Multiple Comparison Tests

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.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Bonferroni Test01:10

Bonferroni Test

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.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DATA-ADAPTIVE EFFICIENT ESTIMATION STRATEGIES FOR BIOMARKER STUDIES EMBEDDED IN RANDOMIZED TRIALS.

The annals of applied statistics·2026
Same author

The interplay between loneliness, cortisol, and NK cell function: The role of cortisol in NK cell dysfunction.

Psychoneuroendocrinology·2026
Same author

NHLBI Workshop Panel Discussion: Summary of the Workshop.

Statistics in medicine·2026
Same author

Sensitivity Analyses for Missing in Repeatedly Measured Outcome Data.

Statistics in medicine·2025
Same author

Mediating Pathways Between Neighborhood Structural Investment and Cardiometabolic Health Across U.S. Cities.

American journal of preventive medicine·2025
Same author

Hierarchical Analysis of Composite Time-to-Event End Points in Heart Failure Clinical Trials Using Time in Clinical State.

Circulation. Heart failure·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Multiple McNemar tests.

Peter H Westfall1, James F Troendle, Gene Pennello

  • 1Area of ISQS, Texas Tech University, Lubbock, Texas 79409-2101, USA. peter.westfall@ttu.edu

Biometrics
|March 30, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for multiple paired proportion tests, controlling the familywise error rate effectively. It offers a closed-form algorithm and a bootstrap alternative for complex data, enhancing statistical reliability.

More Related Videos

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method
12:03

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

Published on: April 18, 2019

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry
12:56

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

Published on: May 13, 2019

Related Experiment Videos

Last Updated: Jun 14, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method
12:03

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

Published on: April 18, 2019

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry
12:56

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

Published on: May 13, 2019

Area of Science:

  • Biostatistics
  • Statistical Methodology
  • Medical Informatics

Background:

  • Performing multiple tests on paired proportions is crucial in various scientific fields.
  • Existing methods may lack robustness or control over error rates in complex scenarios.
  • Accurate statistical testing is vital for reliable conclusions in comparative studies.

Purpose of the Study:

  • To develop a broadly applicable method for multiple tests of paired proportions.
  • To control the familywise error rate (FWER) in the strong sense under minimal assumptions.
  • To provide a closed-form algorithm and a bootstrap alternative for practical application.

Main Methods:

  • Development of a novel method based on McNemar's exact test and exact distributions.
  • Implementation of a closed-form, non-simulation-based algorithm for the proposed method.
  • Creation of a bootstrap alternative to address complex correlation structures in data.

Main Results:

  • The developed method effectively controls the familywise error rate in the strong sense.
  • A simulation study evaluated the operating characteristics of the proposed and other methods.
  • The closed-form algorithm provides an efficient and direct way to perform the tests.

Conclusions:

  • The new method offers a robust and reliable approach for multiple paired proportion testing.
  • Applications include comparing predictive models for disease classification and postmarket surveillance.
  • The study provides valuable tools for researchers needing to perform multiple statistical comparisons.