In this month' s issue of Hospital Pediatrics, Kicker et al 1 shared their experience with a quality improvement (QI) project focused on the elimination of waste: unused propofol after pediatric sedations. After standardizing propofol preparation and ordering, the authors demonstrated a reduction in propofol waste per sedation from a median of 45.6 mL to 14.3 mL. This 68% reduction decreased institutional waste of propofol by an estimated 12.5 L per year. The authors shared steps that can be generalized not just to reduce propofol waste specifically but also to illustrate a methodology that we can use to reduce other medical resource waste, including laboratory overuse, 2,3 chest films in asthma, 4 and bronchodilator and steroid use in bronchiolitis. 5 We feel compelled to emphasize the power of this methodology; specifically, statistical process control (SPC) charts can be used tell QI stories better than traditional statistics, bar charts, or other methods can do alone. What if improvement teams could recognize improvement in a day or week instead of a year? Or identify in real time when outside factors are perturbing their systems? Or predict future performance? In fact, they can. Once understood, SPC allows improvement teams to learn from data in real time, identify variation in data at multiple time points, determine quickly if planned interventions are working, note when nonrandom changes occur, and even (if data are stable) predict future results. 6 Here we highlight these points from Kicker et al' s 1 work to demonstrate how SPC is used to provide superior information for QI when compared with traditional statistics, tables, or pre-and postbar charts. Reporting only traditional statistics, either in simple numerical form or in tables, can be difficult to digest and may lead readers of QI projects to draw incorrect conclusions based on the data presented. 6 Measures of central tendency (mean or median) with an indication of variation (SD or interquartile range) are easily recognized by journal readers, but these may not provide a full understanding of the system being studied and improved. For example, in this study, if the authors had not used SPC, the reader would not have been able to determine how exactly the intervention was temporally related to the change in propofol waste: Was a change in their process already occurring before the intervention? Was there an aberrant peak in waste early in their process that artificially inflated their preintervention data? Was there a new provider who single-handedly lowered the waste who started just before or after the intervention? Imagine that the initial sedation in the baseline data had the highest waste and that the waste was slowly reduced incrementally over time because of other factors. One could