# 9.3: One-Way ANOVA


The purpose of a one-way ANOVA test is to determine the existence of a statistically significant difference among several group means. The test actually uses variances to help determine if the means are equal or not. In order to perform a one-way ANOVA test, there are five basic assumptions to be fulfilled:

1. The null hypothesis is simply that all the group population means are the same. The alternative hypothesis is that at least one pair of means is different. For example, if there are k groups:

$$H_{0} : \mu_{1}=\mu_{2}=\mu_{3}=\ldots \mu_{k}$$

The graphs, a set of box plots representing the distribution of values with the group means indicated by a horizontal line through the box, help in the understanding of the hypothesis test. In the first graph (red box plots), $$H_{0} : \mu_{1}=\mu_{2}=\mu_{3}$$ and the three populations have the same distribution if the null hypothesis is true. The variance of the combined data is approximately the same as the variance of each of the populations.

If the null hypothesis is false, then the variance of the combined data is larger which is caused by the different means as shown in the second graph (green box plots).

Figure $$\PageIndex{3}$$ (a) $$H_0$$ is true. All means are the same; the differences are due to random variation. (b) H0 is not true. All means are not the same; the differences are too large to be due to random variation.

9.3: One-Way ANOVA is shared under a CC BY license and was authored, remixed, and/or curated by .