One independent variable is used in a one-way ANOVA, while two independent variables are used in a two-way ANOVA. Example of a one-way ANOVA As a crop researcher, you want to see how three different fertiliser combinations affect crop output.
The one-way ANOVA (Analysis of Variance) is a parametric test for determining if three or more groups have statistically significant differences in outcomes. ANOVA looks for a general difference, meaning that at least one of the groups is statistically distinct from the rest.
You'd use ANOVA to figure out how your various groups react, with the null hypothesis being that the means of the various groups are equal. If the difference between the two populations is statistically significant, then the two populations are unequal (or different).
The different types of ANOVA are:
General Linear Model.
ANOVA uses an F-test, which tests whether the means of two or more groups are equal. The null hypothesis states that all means are equal, and ANOVA calculates the probability of this happening by using a confidence interval.