An ANOVA (“analysis of variance”) is used to determine whether or not the means of three or more independent groups are equal.
An ANOVA uses the following null and alternative hypotheses:
- H0: All group means are equal.
- HA: At least one group mean is different from the rest.
Whenever you perform an ANOVA, you will end up with a summary table that looks like the following:
|Source||Sum of Squares (SS)||df||Mean Squares (MS)||F||P-value|
Two values that we immediately analyze in the table are the F-statistic and the corresponding p-value.
Understanding the F-Statistic in ANOVA
The F-statistic is the ratio of the mean squares treatment to the mean squares error:
- F-statistic: Mean Squares Treatment / Mean Squares Error
Another way to write this is:
- F-statistic: Variation between sample means / Variation within samples
The larger the F-statistic, the greater the variation between sample means relative to the variation within the samples.
Thus, the larger the F-statistic, the greater the evidence that there is a difference between the group means.
Understanding the P-Value in ANOVA
To determine if the difference between group means is statistically significant, we can look at the p-value that corresponds to the F-statistic.
To find the p-value that corresponds to this F-value, we can use an F Distribution Calculator with numerator degrees of freedom = df Treatment and denominator degrees of freedom = df Error.
For example, the p-value that corresponds to an F-value of 2.358, numerator df = 2, and denominator df = 27 is 0.1138.
If this p-value is less than α = .05, we reject the null hypothesis of the ANOVA and conclude that there is a statistically significant difference between the means of the three groups.
Otherwise, if the p-value is not less than α = .05 then we fail to reject the null hypothesis and conclude that we do not have sufficient evidence to say that there is a statistically significant difference between the means of the three groups.
In this particular example, the p-value is 0.1138 so we would fail to reject the null hypothesis. This means we don’t have sufficient evidence to say that there is a statistically significant difference between the group means.
On Using Post-Hoc Tests with an ANOVA
If the p-value of an ANOVA is less than .05, then we reject the null hypothesis that each group mean is equal.
In this scenario, we can then perform post-hoc tests to determine exactly which groups differ from each other.
There are several potential post-hoc tests we can use following an ANOVA, but the most popular ones include:
- Tukey Test
- Bonferroni Test
- Scheffe Test
Refer to this guide to understand which post-hoc test you should use depending on your particular situation.
The following resources offer additional information about ANOVA tests:
An Introduction to the One-Way ANOVA
An Introduction to the Two-Way ANOVA
The Complete Guide: How to Report ANOVA Results
ANOVA vs. Regression: What’s the Difference?