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 hypothesis testing|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.<ref name="Glennerster-2013">{{Cite book | publisher = Princeton University Press | isbn = 9780691159249 | last = Glennerster | first = Rachel |author2=Kudzai Takavarasha | title = Running randomized evaluations: a practical guide | location = Princeton | date = 2013 |url=https://www.jstor.org/stable/j.ctt4cgd52 |chapter="Chapter 6" }}</ref> | 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 hypothesis testing|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.<ref name="Glennerster-2013">{{Cite book | publisher = Princeton University Press | isbn = 9780691159249 | last = Glennerster | first = Rachel |author2=Kudzai Takavarasha | title = Running randomized evaluations: a practical guide | location = Princeton | date = 2013 |url=https://www.jstor.org/stable/j.ctt4cgd52 |chapter="Chapter 6" }}</ref> |