Key Differences between aov() and anova()

Here’s a detailed difference between aov() and anova() with respect to different parameters.

When to Use aov() in R

1. Use aov() when we want to directly perform an analysis of variance (ANOVA) on our data.
2. It’s useful when you have a simple experimental design with one or more categorical predictor variables and a continuous response variable.
3. aov() accepts formula-based models (response_variable ~ factor1 + factor2) directly.
4. When we have a single model and want to examine the sources of variation and associated statistics, aov() provides a straightforward way to do so.
5. If our analysis goal is primarily to test for differences in means between groups or factors, aov() is sufficient for conducting basic ANOVA tests.

When to Use anova() in R

1. Use anova() when you want to compare the fits of multiple models or factors.
2. It allows you to assess whether adding or removing factors significantly improves the model fit.
3. When you have fitted several models (e.g., with lm() or glm()), anova() helps in comparing these models to see which one best explains the data.
4. anova() offers more flexibility as it can handle comparisons between different types of models, not just ANOVA models.
5. If your analysis requires more advanced statistical comparisons or if you need to assess the significance of interactions between factors, anova() is more suitable

In R Programming Language aov() stands for analysis of variance. It is used to analyze variance. Variance is a statistical technique to compare means among two or more groups. anova() function is used to perform analysis of variance calculation and hypothesis testing. Together both aov() and anova() are used to analyze variance tests in the R Programming Language.