Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means in a sample. ANOVA was developed by the statistician Ronald Fisher. The ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means.
Advantages of ANOVA:
- Robust design
- Increases statistical power In addition a two way ANOVA:
- Looks at interaction between factors
- Reduces random variability
- Can look at effect on second variable after controlling the first variable
Disadvantages of ANOVA:
- If null hypothesis is rejected, we know at least one group differs from others, but with a one way ANOVA and multiple groups, it may be difficult to determine which group is different
- Assumptions need to be fulfilled
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https://en.wikipedia.org/wiki/Analysis_of_variance
https://clinfowiki.org/wiki/index.php/ANOVA
https://www.reneshbedre.com/blog/anova.html