In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be included in the analysis. This results in an important challenge: the need to learn from the data to realize precision gains, but to do so in pre-specified and rigorous way to maintain valid statistical inference. This challenge is especially prominent in cluster randomized trials (CRTs), which often have limited numbers of independent units (e.g., communities, clinics or schools) and many potential adjustment variables.