Statistical Rules of Thumb and the 3.4 dpmo Dilemma

There are a lot of quality consultants out there touting "quick and dirty" statistical methods: easy ways to get an answer without all that inconvenient math. Short course students leave their Become a Statistician in Two Days classes armed with rules of thumb, quick reference guides, and flow charts of simplified methods. Do these methods work? Yes and no. They do perform as advertised: giving an answer without the rigor. But is the answer the right answer?

My question to you is this: In this age of Six Sigma Quality, where only 3.4 dpmo is acceptable (and remember, that is AFTER the 1.5 sigma shift in mean), how is it that we accept statistical rules of thumb to help us make process decisions?

We claim to wholly embrace this 6 Sigma standard, and then turn around and use critical values of +/-2 for our hypothesis tests, or blindly assume normality and happily proceed with our regression modeling. We measure with a micrometer and then cut with an ax.

It is assumed that statistics -- real statistics-- is too difficult a subject for us quality folks to master. Hence a "statistics without the pain" training industry has sprung up to give us "just the facts, nothing more." Why are we selling ourselves short? Sure, statistics requires math and lots of practice. So what? Aren't our customers and their satisfaction worth a little extra training and number crunching?

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© 2016 Mary McShane-Vaughn