Pain relief, or a decrease in self-reported pain intensity, is frequently the primary outcome of pain 10 clinical trials. Investigators commonly report pain relief in one of two ways: using raw units (additive) 11 or using percentage units (multiplicative). However, additive and multiplicative scales have different 12 assumptions and are incompatible with one-another. In this work, we describe the assumptions and 13 corollaries of additive and multiplicative models of pain relief to illuminate the issue from statistical 14 and clinical perspectives. First, we explain the math underlying each model and illustrate these points 15 using simulations, for which readers are assumed to have an understanding of linear regression. Next, we 16 connect this math to clinical interpretations, stressing the importance of statistical models that accurately 17 represent the underlying data; for example, how using percent pain relief can mislead clinicians if the data are actually additive. These theoretical discussions are supported by empirical data from four 19 longitudinal studies of patients with subacute and chronic pain. Finally, we discuss self-reported pain 20 intensity as a measurement construct, including its philosophical limitations and how clinical pain differs 21 from acute pain measured during psychophysics experiments. This work has broad implications for 22 clinical pain research, ranging from statistical modeling of trial data to the use of minimal clinically important differences and patient-clinician communication.