Essentially **Analysis of Variance** (**ANOVA**) is an extension of the two sample hypothesis testing for comparing means (when variances are unknown) to more than two samples. In this part of the website we deal with the simple case, namely **One-way ANOVA**.

Topics:

- Basic Concepts
- Confidence interval
- Experiment-wise error rate
- Planned comparisons
- Unplanned comparisons
- Assumptions
- Homogeneity of variances
- Outliers
- Effect Size
- Power and Sample Size
- Confidence intervals for ANOVA effect size and power
- Kruskal-Wallis Test
- Welch’s Test
- Brown-Forsythe F* Test
- Mood’s Median Test
- Resampling for ANOVA

You have simply the best material for teaching statistics. Thanks a lot for producing this!

Hi Charles:

Thank you very much for this website. I have been benefited from you website in a number of occasions.

I have a question about the ANOVA test:

1. Does it necessary to have the whole population to do Anova or we can as well use the average values of the population to do Anova.

2. I have several p-values from a number of Anova tests. What is the possibility of combining all these p-values to come up with one p-value. Is there any way of averaging the p-value for one system.

Thanks very much.

Subhu,

1. There is no point in running an ANOVA if you have access to the whole population’s data. You can just look at descriptive statistics on the population. If by population, you mean sample, then I am not sure what average values you are referring to. Perhaps a more concrete example would be helpful in understanding what you are trying to accomplish.

2. I can’t see any benefit in averaging p-values. What is it that you are trying to accomplish?

Charles