Infertility RCTs are often too small to detect realistic treatment effects. Large observational studies have been proposed as a solution. However, this strategy threatens to weaken the evidence base further, because non-random assignment to treatments makes it impossible to distinguish effects of treatment from confounding factors. Alternative solutions are required. Power in an RCT can be increased by adjusting for prespecified, prognostic covariates when performing statistical analysis, and if stratified randomization or minimization has been used, it is essential to adjust in order to get the correct answer. We present data showing that this simple, free, and frequently necessary strategy for increasing power is seldom employed, even in trials appearing in leading journals. We use this to motivate a pedagogical discussion and provide a worked example. While covariate adjustment can’t solve the problem of underpowered trials outright, there is an imperative to use sound methodology to maximize the information each trial yields.