首先，你必須確定這些數據是否為這個小組或者個體樣品計算所提供的手段。如果它們為個體樣品（我猜測這個正是你們在談論的情形），這些數據的標準偏差在允許的範圍內被估計，其和數據的序列有關係。所以，如果你改變數據的序列，你將會得到不同的標準偏差。同時，如此不同的 cpk會給不同的規格提供修正。
second, you have to understand thoroughly what cpk is all about. it’s a process capability raticpk=min{cpl,cpu}. it shows how well the process is centered on the target comparing with cp.
so generally people use cp and cpk togather trying to figure out the process capability.
furthermore, there are cases where process capability is low but the process is in control,
and there are cases where the process is out of control but the process capability is comparative high.
these are all related to the variance of the process and how well the process is targeted.
there are lots of misleadings in the use of the process capability ratios in the industries.cp
其次，你必須全面理解什麼是cpk。 它是一個加工能力比率cpk=min{cpl,cpu}，其顯示出該能力是目標中心並優於cp。因此，人們通常將cp和cpk一起使用，並嘗試理解其加工能力。此外，這裡有很多情形就是加工能力低但加工處於控制之中，而加工處於控制之外時加工能力則相對較高。他們與加工的方方面面聯繫，同時加工有很強的目的性。在工業cp中使用加工能力的過程中出現了許多誤導的情形。
in some industries, such as auto industry, people call the calculation of cpk as ppk.
as to why people use 32 or more data to calculate cpk, i did a little research about it. in the industry,
people accept cp 1.33 as a commom sense for existing process which corresponds to 4 sigma variance level.
if you use this date to do a little calculatiuon and check the table published by quality society of america
( i was trying to post that table before, but it didn’t work. it was all messy. i guess the admin deleted that post), you will get the number approximately 32.
but even 32 is not enough sometimes to get a unbiased estimation of the process capability ratio.