ANOVA stands for Analysis of Variance. This is a collection of statistical methods used in experiments that contain multiple treatments and groups. The populations of the samples of collected data may not follow normal distribution curve.
For every ANOVA test, there has to be an experiment designed that enables the use of the process. This involves multiple steps that include:
- subject of investigation
- level of significance
- range of experiment
- Factorials of the experiment
- null and alternative hypothesis
- analysis process
We will learn in depth about experimental design in a later article.
What is ANOVA ?
Analysis of variance process consists of experiments that involve determining the relationship of independent and dependent variables. The variation and difference in means of the variables is the main focus of the experiment. It determines whether there is or isn’t a causal relationship between the variables.
In Regression analysis there are also independent and dependent variables. They can be categorical or numerical. In Analysis of variance there must be numerical variables as categorical variables do not have means and variances.
The majority of analysis of variance tests follow a similar pattern that is given below:
We will be learning about the types of this process as we progress in the topic.
- One – Way
- Two – Way
You Can Learn More about Analysis of Variance here.