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

Significance Testing: Overview01:04

Significance Testing: Overview

6.6K
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...
6.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

3.9K
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).
3.9K
Behrens–Fisher Test00:57

Behrens–Fisher Test

147
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...
147
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.0K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

332
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
332

You might also read

Related Articles

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

Sort by
Same author

Acute effects of daylight saving time clock changes on mental and physical health in England: population based retrospective cohort study.

BMJ (Clinical research ed.)·2025
Same author

Evaluating causal associations of chronotype with pregnancy and perinatal outcomes and its interactions with insomnia and sleep duration: a mendelian randomization study.

BMC pregnancy and childbirth·2024
Same author

A framework for conducting GWAS using repeated measures data with an application to childhood BMI.

Nature communications·2024
Same author

Investigating the Potential Short-term Adverse Effects of the Quadrivalent Human Papillomavirus Vaccine: A Novel Regression Discontinuity Analysis.

Epidemiology (Cambridge, Mass.)·2024
Same author

A hypothesis-free approach to identifying potential effects of relative age in school year: an instrumental variable phenome-wide association study in the UK Biobank.

American journal of epidemiology·2024
Same author

Analyses using multiple imputation need to consider missing data in auxiliary variables.

American journal of epidemiology·2024

Related Experiment Video

Updated: Oct 16, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

A robust mean and variance test with application to high-dimensional phenotypes.

James R Staley1, Frank Windmeijer1,2, Matthew Suderman1

  • 1MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.

European Journal of Epidemiology
|October 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces robust statistical tests to analyze both the mean and variability of health outcomes. These methods, including the joint location-and-scale score test, improve the detection of associations in complex biological data.

Keywords:
ALSPACARIESDNA methylationJoint location-and-scale testVariability test

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.4K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.4K

Related Experiment Videos

Last Updated: Oct 16, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.4K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.4K

Area of Science:

  • Biostatistics
  • Genomics
  • Epigenetics

Background:

  • Traditional health outcome studies focus on mean differences, potentially missing insights from outcome variability.
  • Understanding variability is crucial for identifying effect modifiers and biological mechanisms.
  • High-dimensional data, like DNA methylation, may benefit from tests assessing both location and scale.

Purpose of the Study:

  • To review and develop statistical tests for analyzing the variability (scale) and joint location-and-scale of continuous health outcomes.
  • To introduce a novel joint location-and-scale score (JLSsc) test.
  • To compare the performance of these tests against existing methods.

Main Methods:

  • Review of the Brown-Forsythe test for scale analysis.
  • Development and application of a novel joint location-and-scale score (JLSsc) test.
  • Simulation studies to compare test performance and robustness.
  • Application to epigenome-wide association studies (EWAS) using DNA methylation data from ARIES.

Main Results:

  • The Brown-Forsythe and JLSsc tests maintained correct Type I error rates under non-normal distributions, unlike other tested methods.
  • These robust tests identified over 7500 CpG sites with significant differences in mean or variability of cord blood methylation based on gender or gestational age.
  • The JLSsc test demonstrated improved power in detecting associations beyond mean effects.

Conclusions:

  • The Brown-Forsythe test and JLSsc test are robust and valuable tools for analyzing continuous health outcomes, especially in high-dimensional data.
  • These methods can uncover associations driven by changes in outcome variability, not just mean effects.
  • The developed tests enhance the ability to identify effect modifiers and gain deeper biological insights in fields like epigenetics.