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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

510
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
510
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.4K
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...
4.4K
Bonferroni Test01:10

Bonferroni Test

3.3K
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...
3.3K
Test for Homogeneity01:23

Test for Homogeneity

2.3K
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...
2.3K
Significance Testing: Overview01:04

Significance Testing: Overview

11.3K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
11.3K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Using Importance Sampling to Estimate $p$-values in All-Subset Meta-Analysis, with Applications to Single-Cell eQTL Mapping.

ArXiv·2026
Same author

Semi-targeted Metabolomics Analysis of Biomarkers of Low to Moderate Alcohol Intake in the Postmenopausal Women's Alcohol Study: A Randomized Controlled Crossover Feeding Study.

The Journal of nutrition·2026
Same author

Maternal serum concentrations of persistent organic pollutants and childhood acute lymphoblastic leukemia in the Finnish Maternity Cohort.

Environmental research·2026
Same author

Plasma amino acids in early pregnancy and fetal growth trajectories across pregnancy: findings from a multi-racial U.S. pregnancy cohort.

European journal of nutrition·2026
Same author

Validation of Methylated DNA Markers for Esophageal Squamous Cancer: An International Study.

Cancer prevention research (Philadelphia, Pa.)·2026
Same author

Drinking water nitrate, disinfection byproducts, and prostate cancer incidence in the Agricultural Health Study.

Journal of the National Cancer Institute·2025
Same journal

Conceptualizing Experimental Controls Using the Potential Outcomes Framework.

The American statistician·2025
Same journal

A Cornucopia of Maximum Likelihood Algorithms.

The American statistician·2025
Same journal

A Multiple Imputation Approach for the Cumulative Incidence, with Implications for Variance Estimation.

The American statistician·2025
Same journal

An Example to Illustrate Randomized Trial Estimands and Estimators.

The American statistician·2025
Same journal

Laplace's law of succession estimator and M-statistics.

The American statistician·2025
Same journal

Counternull sets in randomized experiments.

The American statistician·2025
See all related articles

Related Experiment Video

Updated: Jan 2, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K

Revisiting Nested Group Testing Procedures: New Results, Comparisons, and Robustness.

Yaakov Malinovsky1, Paul S Albert2

  • 1Department of Mathematics and Statistics University of Maryland, Baltimore County, Baltimore, MD.

The American Statistician
|December 10, 2019
PubMed
Summary
This summary is machine-generated.

This study compares nested group testing designs for cost savings in disease detection. It introduces optimal procedures and analyzes their robustness when disease prevalence is misestimated.

Keywords:
Coding theoryInformation theoryOptimal design

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.2K
Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
05:57

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Published on: November 20, 2018

58.5K

Related Experiment Videos

Last Updated: Jan 2, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.2K
Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
05:57

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Published on: November 20, 2018

58.5K

Area of Science:

  • Statistics
  • Biostatistics
  • Public Health

Background:

  • Group testing originated in WWII for syphilis detection.
  • Theoretical frameworks developed from the 1950s to 1990s.
  • Renewed interest in group testing for cost-efficiency due to new technologies.

Purpose of the Study:

  • Compare various nested group testing designs.
  • Evaluate Dorfman, Sterrett, and dynamic programming-based optimal procedures.
  • Analyze the robustness of these designs under inaccurate prevalence assumptions.

Main Methods:

  • Developed closed-form expressions for the optimal Sterrett procedure.
  • Provided a literature review of common group testing procedures.
  • Compared nested designs using known and assumed disease prevalence.

Main Results:

  • Presented a technical comparison of nested group testing designs.
  • Quantified the performance of different procedures.
  • Assessed the impact of prevalence misestimation on procedure effectiveness.

Conclusions:

  • Offers a technical and pedagogical resource for group testing.
  • Highlights the importance of accurate prevalence in group testing design.
  • Provides insights into optimizing cost-saving diagnostic strategies.