Variance components are statistical measures that help to understand how much of the total variability in a dataset can be attributed to different sources. In many studies, especially in fields like agriculture or psychology, researchers often want to separate the variability caused by different factors, such as genetic differences or environmental influences. By analyzing variance components, researchers can identify how much each factor contributes to the overall variability. This is particularly useful in mixed models, where both fixed effects (like treatment conditions) and random effects (like individual differences) are considered. Understanding these components aids in better experimental design and interpretation of results.