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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K
Confidence Intervals01:21

Confidence Intervals

7.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
7.1K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.0K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.0K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.5K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.5K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.9K

You might also read

Related Articles

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

Sort by
Same author

On the possibility of hybrid chalcogenide perovskite photovoltaics.

Faraday discussions·2026
Same author

Empirical likelihood inference for the area under the receiver operating characteristic (ROC) curve with verification biased data.

Statistical methods in medical research·2026
Same author

TAMs in the Gynecological Tumor Microenvironment: Insights from Cross-Cancer Studies for Immunotherapy.

Cancers·2026
Same author

Nanoparticle-Induced Breast Cancer Cell Death: The Associated Mechanisms of Seven Major Cell Death Pathways in Preclinical Models and a Cross-Validation Model.

Cells·2026
Same author

6PPD-quinone promotes ovarian cancer progression: Insights from network toxicology, machine learning, and in vitro validation.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
Same author

Methodological Approaches for the Estimation of Confidence Intervals on Partial Youden Index Under Verification Bias.

Pharmaceutical statistics·2026

Related Experiment Video

Updated: Sep 10, 2025

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K

Interval estimation for three-class Youden index with verification bias.

Shuangfei Shi1, Shirui Wang1, Gengsheng Qin1

  • 1Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA.

Journal of Biopharmaceutical Statistics
|August 25, 2025
PubMed
Summary

This study introduces new methods to correct for verification bias in diagnostic accuracy assessments. These techniques improve the selection of optimal cutoff points for medical tests, especially with partially verified disease status.

Keywords:
Bias-correctionYouden indexthree-class classificationverification bias

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.0K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

909

Related Experiment Videos

Last Updated: Sep 10, 2025

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.0K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

909

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Health Services Research

Background:

  • Diagnostic accuracy assessment is crucial for medical tests.
  • Verification bias arises when true disease status is partially unknown, leading to biased evaluations.
  • Existing Youden index methods do not account for this verification bias.

Purpose of the Study:

  • To develop novel confidence intervals for the three-class Youden index.
  • To correct for verification bias in diagnostic accuracy studies.
  • To provide a method for better selection of diagnostic tests.

Main Methods:

  • Development of statistical methods for confidence intervals of the three-class Youden index.
  • Application of methods under the Missing At Random (MAR) assumption for disease status.
  • Focus on diagnostic tests classifying three disease stages.

Main Results:

  • Proposed methods effectively correct for verification bias.
  • New confidence intervals provide more accurate Youden index estimates.
  • The approach leads to improved selection of optimal cutoff points.

Conclusions:

  • The developed methods offer a robust approach to handle verification bias in diagnostic accuracy studies.
  • Accurate assessment of diagnostic tests is enhanced, particularly for three-class scenarios.
  • This work aids in making more informed decisions about diagnostic test utility.