Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Methods to Analyze Parametric Data: ANOVA

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...

What is an ANOVA?

What is an ANOVA?

The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...

What is ANOVA?

What is ANOVA?

The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples be randomly and independently...

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

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:

You might also read

Related Articles

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

Sort by

Same author

Effects of digital cognitive-behavioral therapy for insomnia in heavy drinkers with insomnia: A replication and extension study.

Experimental and clinical psychopharmacology·2026

Same author

Indirect effects of perceived stress and depression on the relationship between insomnia symptoms and hazardous drinking.

Alcohol (Fayetteville, N.Y.)·2025

Same author

Identifying Under- And Overutilization Patterns For Idaho Youth With Serious Emotional Disturbance.

Health affairs (Project Hope)·2024

Same author

Effects of a digital cognitive behavioral therapy for insomnia on sleep and alcohol consumption in heavy drinkers: A randomized pilot study.

Alcohol, clinical & experimental research·2023

Same author

Metabolomic profiling to identify early urinary biomarkers and metabolic pathway alterations in autosomal dominant polycystic kidney disease.

American journal of physiology. Renal physiology·2023

Same author

Evaluation of Telehealth in Child Behavioral Health Services Delivery During the COVID-19 Pandemic.

Psychiatric services (Washington, D.C.)·2022

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

Functional analysis of variance for association studies.

Olga A Vsevolozhskaya¹, Dmitri V Zaykin², Mark C Greenwood³

¹Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America.

|September 23, 2014

Summary

A new statistical method, functional analysis of variance (FANOVA), effectively identifies genetic variants linked to complex diseases. FANOVA outperforms existing methods, especially in smaller studies, improving the discovery of disease-associated genes.

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

Related Experiment Videos

Last Updated: Apr 23, 2026

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Common complex diseases are heritable, but identified genetic variants explain only a small fraction of this heritability.
Next-generation sequencing generates vast amounts of variant data, necessitating advanced statistical methods for analysis.
Discovering novel disease-associated variants remains a challenge in human genetics.

Purpose of the Study:

To introduce a novel statistical method, functional analysis of variance (FANOVA), for testing associations between sequence variants in a genomic region and a qualitative trait.
To evaluate the performance of FANOVA compared to existing methods like SKAT and functional linear models (FLM).

Main Methods:

Functional analysis of variance (FANOVA) method developed for joint testing of common and rare variants.
FANOVA utilizes linkage disequilibrium and genetic position information.
Simulations and empirical studies using Dallas Heart Study sequencing data.

Main Results:

FANOVA demonstrated superior performance over SKAT and FLM in simulations, particularly for small sample sizes or low-to-moderate effect variants.
In an empirical study, FANOVA identified both ANGPTL4 and ANGPTL3 genes associated with obesity, whereas SKAT and FLM identified them separately.

Conclusions:

FANOVA is a powerful and computationally efficient method for identifying genetic variants associated with complex diseases.
The method enhances the discovery of novel disease-associated genes by considering joint effects and utilizing sequence variant information comprehensively.