Developments in Analysis of Variance (ANOVA) and Experimental Design: A Comprehensive Overview
Abstract
Experimental design and analysis of variance (ANOVA) are foundational concepts in the realm of scientific research, providing researchers with powerful tools to explore relationships between variables and draw meaningful conclusions from their data. This comprehensive review article aims to delve into the recent advancements in experimental design and ANOVA methodologies, highlighting their applications across diverse fields and shedding light on emerging trends. By scrutinizing the fundamental principles of experimental design, including factors, levels, and randomization, we emphasize the importance of constructing robust experiments. The article then shifts focus towards recent innovations, such as factorial designs and fractional factorial designs, which enable researchers to efficiently explore multiple factors simultaneously.
In the realm of statistical analysis, ANOVA serves as a cornerstone technique rooted in the principles of variance decomposition. This review provides an overview of traditional one-way ANOVA and delves into its extensions, including two-way and multivariate ANOVA. Recent developments in non-parametric ANOVA and Bayesian ANOVA are explored, showcasing their utility in handling non-normal data distributions and incorporating prior knowledge into the analysis.
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