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

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.8K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
27.8K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

542
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%...
542
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
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...
6.1K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.7K
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.
5.7K
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
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

You might also read

Related Articles

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

Sort by
Same author

PANDA pediatric arousal neural detection architecture.

NPJ digital medicine·2026
Same author

Between Patterns and Predictions: Interpretable Latent EEG Representations for Clinical Insights.

medRxiv : the preprint server for health sciences·2026
Same author

BCGNet: an AI model trained on 600 K hours of sleep data for a novel under-pillow contactless monitoring device.

NPJ digital medicine·2026
Same author

Association between Interictal Spike Rate and Seizure Frequency in a Large Epilepsy Cohort.

medRxiv : the preprint server for health sciences·2026
Same author

Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.

Journal of medical Internet research·2026
Same author

Quantification of Ventilatory Control in Sleep Apnea: From Physiological Insight to Computable Loop Gain.

Sleep·2026

Related Experiment Video

Updated: Jan 5, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Asymptotic Geometry of Multiple Hypothesis Testing.

M Brandon Westover1

  • 1Department of Neurology, Massachusetts General Hospital, Boston, MA 02114-2622 USA.

IEEE Transactions on Information Theory
|October 15, 2019
PubMed
Summary
This summary is machine-generated.

We found a simple geometric way to understand multiple hypothesis testing. The best strategy involves a nearest neighbor approach on the probability simplex for asymptotic problems.

Keywords:
Geometryhypothesis testinglarge deviationspattern recognition

More Related Videos

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

18.9K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Related Experiment Videos

Last Updated: Jan 5, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

18.9K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Multiple hypothesis testing is crucial in data analysis.
  • Existing methods can be complex to interpret.

Purpose of the Study:

  • To provide a simple geometrical interpretation for multiple hypothesis testing solutions.
  • To identify the optimal decision rule in the asymptotic limit.

Main Methods:

  • Geometrical interpretation of the multiple hypothesis testing problem.
  • Analysis in the asymptotic limit.

Main Results:

  • The optimal decision rule is a nearest neighbor classifier.
  • This classification occurs on the probability simplex.

Conclusions:

  • A clear geometrical framework simplifies understanding of multiple hypothesis testing.
  • Nearest neighbor classification offers an intuitive approach to decision rules.