You may override this value.">Effect Size:
The ANOVA test checks if the difference between the averages of two or more groups is significant, using sample data.
ANOVA is usually used when there are at least three groups since for two groups, the two-tailed pooled variance t-test and the right-tailed ANOVA test have the same result.
The basic ANOVA test contains only one categorical value, one-way ANOVA. For example, if you compare the performence of three schools, the categorical variable is school, and the possible values of the categorical variable are School-A, School-B, School-C. There are more complex ANOVA tests that contain two categorical variables ( Two-way ANOVA calculator ), or more. When performing a one-way ANOVA test, we try to determine if the difference between the averages reflects a real difference between the groups, or is due to the random noise inside each group.
The F statistic represents the ratio of the variance between the groups and the variance inside the groups. Unlike many other statistic tests, the smaller the F statistic the more likely the averages are equal.
Example: Compare four fertilizers used in four fields
H0: The average weight of crops per square meter is equal in all fields.
H1: At least one field yields a different average per square meter.
|Source||Degrees of Freedom||Sum of Squares||Mean Square||F statistic||p-value|
|k - 1||MSG = SSG / (k - 1)||F = MSG / MSE||P(x > F)|
|n - k||MSE = SSE / (n - k)|
|Total||n - 1||SS(total) = SSG + SSE||Sample Variance = SS(total) / (n - 1)|