In the context of a real-world recruitment experiment with 3,305 public schools, we systematically analyze the empirical importance of standard conditions for the validity and generalizability of field experiments - the internal and external overlap and the “no-site selection bias” conditions – and show ways to address them. We experimentally vary the degree of overlap in disjoint sub-samples from the recruitment experiment, mimicking small-scale field experiments. This we achieve by using different treatment assignment techniques, among them the novel minMSE method which accounts for characteristics of the covariate distributions beyond mean values. We then link overlap and covariate balance to the precision of treatment effect estimates from the recruitment experiment, and find that the minMSE treatment assignment method improves overlap and reduces bias by more than 35% compared to pure randomization. Analyzing self-selection of schools in the recruitment experiment with rich administrative data on institution and municipality characteristics, we find no evidence for a site-selection bias.