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Statistical process control (SPC) is a critically important approach for measurement and analysis in quality improvement. As initially developed by Shewhart,1 applied by Deming2 and later modernised and made more accessible by many others,3–9 SPC provides a statistically powerful yet visually elegant and inclusive approach to assessing performance variation to inform intelligent action. It has a substantial range of applications, from simple practical uses by frontline improvement teams to much more sophisticated applications,10 11 and can complement inferential analytical methods in mixed-methods approaches that can be used to inform improvement in health services research.12 A highly useful aspect of SPC is its ability to detect special cause signals—non-random variations that occur in a process compared with a reference measure of central tendency (a mean or median). This can help improvers assess whether tests of change are resulting in improvements, and if new performance levels currently observed will sustain over time.