In experimental social science, precise treatment effect estimation is of utmost importance, and researchers can make design choices to increase precision. Specifically, block-randomized and pre-post designs are promoted as effective means to increase precision. However, implementing these designs requires pre-treatment covariates, and collecting this information may decrease sample sizes, which in and of itself harms precision. Therefore, despite the literature’s recommendation to use block-randomized and pre-post designs, it remains unclear when to expect these designs to increase precision in applied settings. We use real-world data to demonstrate a counterintuitive result: precision gains from block-randomized or pre-post designs can withstand significant sample loss that may arise during implementation. Our findings underscore the importance of incorporating researchers’ practical concerns into existing experimental design advice.
