Clinical trials often demonstrate treatment efficacy through change in forced expiratory volume in one second (FEV1), comparing single FEV1 measurements from post- versus pre-treatment timepoints. Day-to-day variation in measured FEV1 is common for reasons such as diurnal variation and intermittent health changes, relative to a stable, monthly average. This variation can alter estimation of associations between change in FEV1 and baseline in predictable ways, through a phenomenon called regression to the mean. We quantify and explain day-to-day variation in percent-predicted FEV1 (ppFEV1) from 4 previous trials, and we present a statistical, data-driven explanation for potential bias in ceiling and floor effects due to commonly observed amounts of variation. We recommend accounting for variation when assessing associations between baseline value and change in CF outcomes in single-arm trials, and we consider possible impact of variation on conventional standards for study eligibility.