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

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...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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).
Behrens–Fisher Test00:57

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...
Fisher's Exact Test01:08

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...
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

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 data...
Multiple Comparison Tests01:13

Multiple Comparison Tests

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...

You might also read

Related Articles

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

Sort by
Same author

Ethical implications of high attrition in AI-based mental health interventions: a systematic review and meta-analysis.

BMC medical ethics·2026
Same author

Hybrid Supervised-Unsupervised Modeling for Post-Hurricane Private Well Contamination Risk Score Using Empirical Validation and Community-Informed Assessment.

GeoHealth·2026
Same author

A 35-Year-Old Woman With Recurrent Hypertriglyceridemia-Induced Pancreatitis in Pregnancy Managed With Fenofibrate and Plasmapheresis.

The American journal of case reports·2026
Same author

Precise prevention of DEHP induced hepatic fibrosis: Early identifying high-risk populations, revealing key factors, and applying targeted intervention.

Journal of environmental sciences (China)·2026
Same author

Retraction: JQ1 suppresses tumor growth through downregulating LDHA in ovarian cancer.

Oncotarget·2026
Same author

Retraction: JQ1 suppresses tumor growth via PTEN/PI3K/AKT pathway in endometrial cancer.

Oncotarget·2026
Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026
Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026
Same journal

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same journal

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.

Advances in neural information processing systems·2026
Same journal

Emergence and Evolution of Interpretable Concepts in Diffusion Models.

Advances in neural information processing systems·2026
See all related articles

Related Experiment Video

Updated: May 21, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Efficient Moments-based Permutation Tests.

Chunxiao Zhou1, Huixia Judy Wang, Yongmei Michelle Wang

  • 1Dept. of Electrical and Computer Eng., University of Illinois at Urbana-Champaign, Champaign, IL 61820.

Advances in Neural Information Processing Systems
|June 21, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient moments-based permutation test, approximating distributions with Pearson series for improved computational speed. The novel method enhances accuracy and efficiency in statistical testing using simulated and real data.

Related Experiment Videos

Last Updated: May 21, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Permutation tests are valuable nonparametric methods but can be computationally intensive.
  • Approximating permutation distributions can offer efficiency gains.

Purpose of the Study:

  • To develop a computationally efficient moments-based permutation test.
  • To enhance the speed of permutation tests without sacrificing accuracy.

Main Methods:

  • Approximation of the permutation distribution using Pearson distribution series.
  • Calculation of the first four moments of the permutation distribution.
  • A novel recursive method for theoretical and analytical derivation of moments.

Main Results:

  • The proposed method demonstrates improved computational efficiency.
  • Accuracy is maintained through the combination of nonparametric and parametric approaches.
  • Validation using simulated and real datasets with various test statistics.

Conclusions:

  • The moments-based permutation test offers a balance of accuracy and computational efficiency.
  • This approach provides a practical alternative for large datasets where traditional permutation tests are infeasible.