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

F Distribution01:19

F Distribution

The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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...

You might also read

Related Articles

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

Sort by
Same author

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

Journal of the American Statistical Association·2026
Same author

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026
Same author

Ultrasound-Guided Truncal Blocks for Tube Thoracostomy Analgesia in the Emergency Department.

The Journal of emergency medicine·2026
Same author

Discrimination between domestic and imported red chili powders using laser-induced breakdown spectroscopy: variable selection and comparative evaluation of machine learning algorithms.

Analytical methods : advancing methods and applications·2026
Same author

A Complex Case of Refractory Exertional Compartment Syndrome With Fibular Neuropathy in an Athlete.

Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine·2026
Same author

High-throughput identification of geographical origins of rubies using hyperspectral visible and fluorescence spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
Same journal

Post-selection inference for causal effects after causal discovery.

Biometrika·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Finding distributions that differ, with false discovery rate control.

Yonghoon Lee1, Edgar Dobriban1, Eric J Tchetgen Tchetgen1

  • 1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, Philadelphia, Pennsylvania 19104, U.S.A.

Biometrika
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces batch conformal p-values for comparing distributions, offering exact, distribution-free false discovery rate control. This method identifies differing groups and shows strong performance in simulations and real-world datasets.

Keywords:
Distribution-free inferenceFalse discovery rateMultiple testing

More Related Videos

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Related Experiment Videos

Last Updated: Jun 11, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Comparing multiple distributions to a reference is crucial in data analysis.
  • Existing methods may lack robustness or require distributional assumptions.

Purpose of the Study:

  • To develop a novel methodology for identifying comparison groups with distributions differing from a reference group.
  • To provide exact, distribution-free control of the false discovery rate (FDR) in multiple-testing scenarios.

Main Methods:

  • Introduction of 'batch conformal p-values'.
  • Demonstration of positive regression dependence across groups, enabling FDR control via the Benjamini-Hochberg procedure.
  • Novel proof technique for rank vector construction under exchangeability.

Main Results:

  • The proposed method achieves exact, distribution-free FDR control.
  • Simulations show performance comparable to distribution-specific methods and superior power over direct conformal detection.
  • Application to hepatitis C and Current Population Survey datasets identified significant patient and subpopulation groups.

Conclusions:

  • Batch conformal p-values offer a powerful, distribution-free approach for multiple distribution comparison.
  • The methodology is effective in identifying meaningful differences in real-world data.
  • This work advances FDR control techniques in statistical inference.