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

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Significance Testing: Overview01:04

Significance Testing: Overview

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...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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...

You might also read

Related Articles

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

Sort by
Same author

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

PLoS neglected tropical diseases·2026
Same author

Pooled testing for TB: revisiting a cost-saving innovation.

The Lancet. Respiratory medicine·2025
Same author

Performance of Urine Reagent Test Strips in Detecting <i>Schistosoma haematobium</i> Infection in Individual and Pooled Urine Samples.

Microorganisms·2025
Same author

A mixed-effects Bayesian regression model for multivariate group testing data.

Biometrics·2025
Same author

Bayesian Additive Regression Trees for Group Testing Data.

Statistics in medicine·2025
Same author

Regression analysis of group-tested current status data.

Biometrika·2024
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2026

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

Informative Retesting.

Christopher R Bilder1, Joshua M Tebbs, Peng Chen

  • 1Associate Professor of Statistics ( chris@chrisbilder.com , Website: www.chrisbilder.com ) at the University of Nebraska-Lincoln, Lincoln, NE 68583.

Journal of the American Statistical Association
|November 30, 2010
PubMed
Summary
This summary is machine-generated.

Group testing benefits infectious disease screening with limited resources. A new "informative retesting" method uses individual data to reduce tests while maintaining accuracy.

More Related Videos

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice
07:18

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

Published on: March 2, 2019

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory
07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Published on: June 14, 2019

Related Experiment Videos

Last Updated: Jun 6, 2026

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats

Published on: June 18, 2018

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice
07:18

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

Published on: March 2, 2019

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory
07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Published on: June 14, 2019

Area of Science:

  • Biostatistics
  • Public Health
  • Infectious Disease Epidemiology

Background:

  • Group testing offers efficiency in screening large populations for diseases.
  • Traditional group testing methods often overlook individual variations, potentially impacting test efficiency.
  • Limited resources necessitate optimized screening strategies in public health.

Purpose of the Study:

  • To introduce a novel group testing approach called "informative retesting."
  • To leverage individual covariate information for enhanced retesting strategies.
  • To reduce the total number of tests required for accurate disease identification.

Main Methods:

  • Developed a new group testing framework incorporating individual covariate data.
  • Derived closed-form expressions for test count probability mass functions.
  • Applied and validated the informative retesting approach using real-world data.

Main Results:

  • Informative retesting significantly decreases the number of tests needed.
  • Accuracy remains comparable to existing non-informative retesting procedures.
  • Demonstrated effectiveness in chlamydia and gonorrhea screening.

Conclusions:

  • Informative retesting provides a more efficient and accurate method for group testing.
  • This approach is particularly valuable in resource-limited infectious disease screening programs.
  • Covariate information can be effectively utilized to optimize diagnostic testing strategies.