“It could be argued that the use of and C_{pk} (with sufficient sample size) are far more valid estimates of long and short term capability of processes since the 1.5 sigma shift has a shaky statistical foundation.” Eoin

“C_{pk} tells you what the process is CAPABLE of doing in future, assuming it remains in a state of statistical control. tells you how the process has performed in the past. _{pk}, because the process is not in a state of control. The values for C_{pk} and will converge to almost the same value when the process is in statistical control. that is because sigma and the sample standard deviation will be identical (at least as can be distinguished by an F-test). When out of control, the values will be distinctly different, perhaps by a very wide margin.” Jim Parnella

“C_{p} and C_{pk} are for computing the index with respect to the subgrouping of your data (different shifts, machines, operators, etc.), while P_{p} and are for the whole process (no subgrouping). For both and C_{pk} the ‘k stands for ‘centralizing facteur it assumes the index takes into consideration the fact that your data is maybe not centered (and hence, your index shall be smaller). It is more realistic to use P_{p} and than C_{p} or C_{pk} as the process variation cannot be tempered with by inappropriate subgrouping. However, C_{p} and C_{pk} can be very useful in order to know if, under the best conditions, the process is capable of fitting into the specs or not.It basically gives you the best case scenario for the existing process.” Chantal

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