The updating of the spreadsheets for analysing controlled trials to include adjustment for a single covariate represents a genuine advance in Will Hopkins’ mission to provide robust yet user-friendly analysis tools for the non-expert. Even in a relatively large randomised controlled trial (RCT), there may be chance imbalances across the trial arms for an important covariate. This potential problem applies also to chance imbalances at baseline for the primary outcome variable. Hence, in pretest-posttest RCTs a spreadsheet that permits the inclusion of the pre-test score as the covariate is a valuable tool. (As Hopkins points out, differences between intervention and control groups in the mean value of a covariate may be due also to poor randomization or selective drop-out of participants.) Further, Hopkins makes a key point that is often under-appreciated in the analysis of RCTs: when the covariate interacts with the treatment, including the covariate in the analysis may improve the precision of estimation of the mean intervention effect, even in the absence of substantial differences in the mean for the covariate between trial arms.