 EBM’s reliance on randomized control trials as a cornerstone of health data collection doubles down on the general reductionism of biomedicine. Randomized control trials assume that health variables can be observed, manipulated and controlled as largely independent units. Yet, is a well-established problem that we simply don’t know all of the variables that might confound a clinical experiment (Worrall 2007), so randomization can at most hope that the ‘confounders’ (e.g., unknown dietary factors that affect metabolization of a drug) are randomly distributed between the different treatment populations in an experiment. Taking the reductionism a step further, the goal of exactly measuring the differences between Treatment A and Treatment B requires EBM to only look for effects that are precisely measurable in the first place. This limitation is a deeply-rooted problem since even the official list of clinical signs and symptoms of a given disease can diverge from accumulated patient observations, and in cases such as some psychiatric conditions, the symptoms—or lack thereof—are overtly difficult to measure or factor into an assessment of treatment efficacy (e.g., one’s sense of self and/or emotional life) (Kendler 2016).