In the causal inference literature, evaluating blocking from a potential outcomes perspective has two main branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different estimators for the standard errors of the estimated average impact, but they are also built on different sets of assumptions. Additionally, neither literature handles cases with blocks of varying size that contain singleton treatment or control units, a case which can occur with different forms of matching or post-stratifcation. Differences in the two literatures have also created some confusion regarding the benefits of blocking in general. In this paper, we first reconcile the literatures by carefully examining the performance of different estimators of treatment effect and of associated variance estimators under several different frameworks. We then use these insights to derive novel variance estimators for experiments containing blocks of different sizes. We also assess in which situations blocking is not guaranteed to reduce precision.