Estimation of empirical relationships is prone to bias. Economists have carefully studied sources of bias in structural and quasi-experimental approaches, but the randomized control trial (RCT) has only begun to receive such scrutiny. In this paper, we argue that several lessons from medicine, derived from analysis of thousands of RCTs establishing a clear link between certain practices and biased estimates, can be used to reduce the risk of bias in economics RCTs. We identify the subset of these lessons applicable to economics and use them to assess risk of bias in estimates from economics RCTs published between 2001 and 2011. In comparison to medical studies, we find most economics studies do not report important details on study design necessary to assess risk of bias. Many report practices that suggest risk of bias, though this does not necessarily mean bias resulted. We conclude with suggestions on how to remedy these issues.