To go beyond the basic, one should contemplate the concept "orthogonality," which is important to both ANOVA and regression. In ANOVA when group sizes are balanced, the design is said to be orthogonal. In regression when predictors are not inter-related, they are also said to be orthogonal. Experimental design could be conceptualized as model building . In this sense, relationships among variables are specified to form a model. "Non-orthogonal" variables are detrimental to from a sound model. A Director program entitled "Collinearity: A question of support," which was developed by myself, David Winograd, Sandra Andrews, Samuel DiGangi, and Angel Jannasch (1999), explains both "variance" and "orthogonality" in layman terms. A web version entitled "Mutli-collinearity, orthogonality, variance inflation factor" (Yu, 2016), which carries more detail, is also available.
The number of treatment units (subjects or groups of subjects) assigned to control and treatment groups, affects an RCT's reliability. If the effect of the treatment is small, the number of treatment units in either group may be insufficient for rejecting the null hypothesis in the respective statistical test . The failure to reject the null hypothesis would imply that the treatment shows no statistically significant effect on the treated in a given test . But as the sample size increases, the same RCT may be able to demonstrate a significant effect of the treatment, even if this effect is small.