Research Topic · Peer-Reviewed

Analysis of Variance

Analysis of variance (ANOVA) is a statistical method for testing whether the means of three or more groups differ significantly by partitioning total variability in the data into between-group and within-group components. The resulting F-statistic compares the variance explained by group membership with residual var…

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 24× across the literature 🔖 ISSN 2643-2811 🗓 Reviewed July 2026

Overview

Analysis of variance (ANOVA) is a statistical method for testing whether the means of three or more groups differ significantly by partitioning total variability in the data into between-group and within-group components. The resulting F-statistic compares the variance explained by group membership with residual variance; a significant result indicates that at least one group mean differs, prompting post-hoc comparisons to locate specific differences. One-way ANOVA evaluates a single factor, while factorial and two-way designs assess multiple factors and their interactions, and related procedures handle genotype-by-environment effects and repeated measures. Valid use depends on assumptions of independence, approximate normality of residuals, and homogeneity of variances, with transformations or alternatives applied when these are violated. The research in this area applies ANOVA and comparative analysis to agronomic trials evaluating crop growth, yield stability, and genotype-by-environment interaction, to comparisons of atherosclerosis risk factors across occupational groups, biodegradation and process-optimisation experiments, and stratified analyses of clinical outcomes. ANOVA matters because it provides a rigorous framework for comparing multiple conditions simultaneously while controlling the error rate, making it foundational to experimental design and hypothesis testing across the agricultural, biological, medical, and behavioural sciences. The journal publishes peer-reviewed research that employs analysis of variance and related comparative statistics across these domains.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 12 articles above have been cited 24 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Analysis of Variance, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Model Based Research (ISSN 2643-2811).

Journal editorial board
Yoshiaki Kikuchi · Japan Yung-Yao Chen · Taiwan Yang Chen · United States

This page summarises published research for orientation; it is not medical or professional advice.