It is a value calculated by dividing treatment mean squares by error mean squares, then compared to F table found from d.f. of treaments and error. The larger the value of F ratio, the higher the significance we expect.

In ANOVA (analysis of variance), the F value is a test statistic that measures the ratio of between-group variability to within-group variability.

The F value is calculated by dividing the variance between the group means by the variance within the groups. The F value is then compared to a critical value from an F-distribution to determine if the difference between group means is statistically significant or not.

In ANOVA, the null hypothesis is that the means of all groups are equal, and the alternative hypothesis is that at least one group mean is different from the others. The F value is used to determine if the null hypothesis should be rejected or not.

If the F value is large (i.e., the between-group variability is much greater than the within-group variability), then the null hypothesis can be rejected, and it can be concluded that there is a significant difference between the group means. On the other hand, if the F value is small (i.e., the between-group variability is similar to the within-group variability), then the null hypothesis cannot be rejected, and it can be concluded that there is no significant difference between the group means.

The F value is a key output of ANOVA, and it is used to make inferences about the differences between group means. It is also used to calculate the p-value, which indicates the probability of observing the data if the null hypothesis were true. The smaller the p-value, the stronger the evidence against the null hypothesis and in favor of the alternative hypothesis.