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

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
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Cochran's Q Test01:17

Cochran's Q Test

853
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...
853
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

4.8K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
4.8K
Microsoft Excel: Student's t-Test01:25

Microsoft Excel: Student's t-Test

1.3K
Student's t-test in Microsoft Excel is a statistical method used to compare the means of two groups to determine if they are significantly different from each other. It's commonly used to evaluate hypotheses, such as testing whether a treatment has an effect compared to a control group. Excel provides built-in functions to perform t-tests, making it accessible for users needing to conduct basic statistical analysis.
To conduct a t-test in Excel, use the T.TEST function or the "Data...
1.3K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same author

Factors affecting detection and quantification of Schistosoma haematobium eggs in pooled urine samples.

PLoS neglected tropical diseases·2026
Same author

Two case reports on the identification and management of hypervirulent <i>Klebsiella pneumoniae</i> isolated in Nebraska, USA.

ASM case reports·2026
Same author

Assessing Simultaneous Infection with Multiple Pathogens via Group Testing with Imperfect Multiplex Assays.

Journal of agricultural, biological, and environmental statistics·2026
Same author

Consumption of Reinforcing Solutions Engages Dynamic Activity of the Prelimbic Cortical Outputs.

bioRxiv : the preprint server for biology·2026
Same author

Granular insights: A wastewater-based machine learning approach for localized COVID-19 hospitalization forecasting.

Epidemics·2026
Same journal

No Fans, No Home Advantage?

Significance (Oxford, England)·2023
Same journal

The Spectre of Berkson's Paradox: Collider Bias in Covid-19 Research.

Significance (Oxford, England)·2023
Same journal

After Covid-19, The US Statistical System Needs to Change.

Significance (Oxford, England)·2023
Same journal

Covid-19 Through the Lens of the Peer-Reviewed Literature.

Significance (Oxford, England)·2023
Same journal

Science After Covid-19: Faster, Better, Stronger?

Significance (Oxford, England)·2023
Same journal

The real scale of domestic violence during Covid-19.

Significance (Oxford, England)·2022
See all related articles

Related Experiment Video

Updated: Dec 18, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K

Tests in short supply? Try group testing.

Christopher R Bilder1, Peter C Iwen2, Baha Abdalhamid3

  • 1Professor in the Department of Statistics at the University of Nebraska-Lincoln.

Significance (Oxford, England)
|June 16, 2020
PubMed
Summary
This summary is machine-generated.

Pooling specimens for SARS-CoV-2 testing can significantly increase laboratory capacity. This strategy allows for more efficient processing of large numbers of samples, aiding in pandemic response efforts.

More Related Videos

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.3K
A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents
06:15

A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents

Published on: July 10, 2017

13.3K

Related Experiment Videos

Last Updated: Dec 18, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
08:26

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

1.9K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.3K
A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents
06:15

A Modified Trier Social Stress Test for Vulnerable Mexican American Adolescents

Published on: July 10, 2017

13.3K

Area of Science:

  • Clinical diagnostics
  • Virology
  • Public health

Background:

  • The COVID-19 pandemic necessitated rapid scaling of diagnostic testing capacity.
  • Individual specimen testing can be a bottleneck in high-throughput laboratory settings.

Purpose of the Study:

  • To evaluate the effectiveness of specimen pooling for increasing SARS-CoV-2 testing capacity.
  • To determine the feasibility of pooling strategies in real-world laboratory conditions.

Main Methods:

  • Specimens were collected and batched into defined pools for unified testing.
  • Standardized molecular assays were employed for pooled and individual specimen analysis.
  • Performance metrics including sensitivity and specificity were assessed for pooled testing.

Main Results:

  • Specimen pooling demonstrated a significant increase in the number of samples that could be processed.
  • The sensitivity of pooled testing remained comparable to individual testing under optimal conditions.
  • The efficiency gains were most pronounced at higher prevalence rates.

Conclusions:

  • Specimen pooling is a viable strategy to enhance SARS-CoV-2 testing throughput.
  • This approach can optimize resource allocation in public health laboratories during outbreaks.
  • Further validation is recommended for diverse testing platforms and population prevalences.