Final answer:
A repeated measures ANOVA does not consider between-subjects variability; it focuses on within-subjects variability. Homogeneity of variance and interaction effects must be considered to ensure the validity of this statistical analysis.
Step-by-step explanation:
In repeated measures ANOVA, what is not considered is a. Between-subjects variability. This type of analysis focuses on the variability within subjects because the same subjects are measured multiple times under different conditions. The within-subjects variability is important because it can show if there is a significant effect due to the treatment or condition. Homogeneity of variance is an assumption that needs to be checked because it concerns whether the variances across the different measures are similar. Interaction effects also have to be considered since they provide information on whether the effect of one independent variable differs across the levels of another independent variable.
Noteworthy points include the influence of population variance which could be affected by genetic structure, environment, diet composition, and other factors. Moreover, when comparing two proportions, as in the case of a pharmaceutical company measuring the efficacy of a new drug compared to a placebo, understanding the type of variability that contributes to the outcome is vital - here it would be population differences among means or population variance as related to the treatment or control conditions.