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Statistical control charts are the most commonly used tools to analyze and monitor process variation and stability. Control charts help us isolate nonrandom causes of variation by plotting a measured attribute of the process over time; the upper and lower control limits are empirically derived from the measurements of process variation over time. If a data point falls outside the control limits, we assume that a nonrandom cause of variation is present. It is important that the control limits appropriately reflect the expected behavior of the process being measured. Measuring the number of escaped defects will alert us to problems in the inspection process even though the control charts might not be showing anything abnormal.