Not only do factorial designs allow us to assess the interaction between the two independent variables,but they also "remove" the individual and joint effects of these variables from the:
A)between-group variability
B)F ratio
C)within-group variability
D)sum of squares total

Question

Quizplus · Accepted Answer

Within-group variability (or error variance) is the variance that cannot be attributed to the independent variables and their interaction. By removing the individual and joint effects of the independent variables, factorial designs reduce the within-group variability and make it easier to detect significant effects of the independent variables and their interaction. By contrast, the between-group variability measures the differences in the means of the groups defined by the independent variables, and the sum of squares total is the sum of the within-group and between-group variability. The F ratio compares the between-group and within-group variability to test the null hypothesis that there are no effects of the independent variables or their interaction.

Not Only Do Factorial Designs Allow Us to Assess the Interaction