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When you conduct an analysis of variance, what you are doing is comparing and contrasting your original hypothesis against your actual findings. The variance is your experimental group versus your control group.
For instance, if I want to see how chocolate milk affect kids in the morning, I would need a control group *kids drinking chocolate milk regularly* versus an experimental one *kids being refrained from drinking it*
When you compare their similarities and differences, you are conducting an analysis of variance.
Analysis of variance often abbreviated as ANOVA is a statistical technique used to test for significance of difference among more than two sample means. Using this techniques it is possible to draw inference about whether different sampled drawn have same mean. For example this method may be used in studies such as comparing intelligence of students from different schools.
When using analysis of variance it is assumed that each of the sample is is drawn from population having normal distribution with same variance. The assumption of normality is not required when the sample size is large.
The analysis of variance is carried out in following three steps:
- Population variance is estimated by variance among sample means.
- A second estimate of variance is made form variance within samples.
- Comparing these two estimates of variance. If they are approximately equal in value it is inferred that the the means are not significantly different.
The ANOVA is a set of different procedures which can be used for various procedures, such as to compare means. There are different models of ANOVA. Some models assume that you are sampling from all the available data while others do not. It's like saying the doctor found out you were sick. You still don't know what you have.
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