在諸如汽車業的一些產業中，人們將對cpk的計算稱作ppk。至於人們為什麼用32或者更多的數據來計算cpk，我對此做了一些研究。人們在運算中視cp1。33為普通理解與當前能力與4sigma的離差保持一致。如果你用這個數據做一些計算然後對照美國質量出版社出版的表格。（我曾嘗試著郵寄那張表，但都沒有成行。這簡直太糟了，我猜想管理部門遺失了該郵件）。你可以取值接近32，但即使32有時候也不足以得到一個沒有誤差的加工能力比率
what i wanna stress again is that capability ratio is not everything, there are too many misuses in the industry, don’t count all on it.我想再一次強調的是加工能力比率並不是萬能的，在工業上有很多的誤用，不要全部依靠它來計算。
here is my answer to the question of 32 sample size:這裡是我對樣本尺寸為32的問題的回答。
a practice that is increasingly common in industry is to require a supplier to demonstrate process capability as part of the
contractual agreement.
thus, it is frequently necessary to prove that the process capability ratio cp meets or exceeds some particular target value---say, cp0. this problem may be formulated as a hypothesis testing problem:
一個要在工業中日漸成熟的練習是需要一個供應者示範如契約的協定部份般的程式能力。 因此，有必要經常證明加工能力比率cp等於或者超過如cp0的一些特殊目標價值。這個問題可能被制定為一個假設的測試問題:
h0: cp= cp0 (or the process is not capable)
h1: cp≥ cp0 (or the process is capable)
we would like to reject h0 (recall that in statistical hypothesis testing rejection of null hypothesis is always a strong conclusion),
thereby demonstrating that the process is capable. we can formulate the statistical test in terms of cp’,
so that we will reject h0 if cp’ exceeds a critical value c.
我們想要否定h0( 取消對統計的假設中無效力假設的測試否定一直是一個強大的結論)。因此，示範加工是有能力的。我們可以根據 cp’ 制定統計的測試, 所以如果 cp’超過一個關鍵的價值 c，那么我們會否定h0 。
kane(1986) has investigated this test, and provide a table of sample sizes and critical values for c to assist in testing process capability.
we may define cp(high) as a process capability that we would like to accept with probability (1-α) and cp(low) as a process capability that we’d like to reject with probability (1-β). please refer to the table created by kane and used by american society for quality control.
凱恩 (1986) 已經調查這上述測試, 而且向c提供一張有樣品大小和關鍵值的表給來協助測試的加工能力。就如我們喜歡接受(1-α)的可能性和cp（低）作為程式能力和否定(1-β)的可能性一樣，我們可以將cp（高）定義為一個加工能力。請查閱凱恩所創建的並為美國社會質量控制所用的表格