Stakeholder Analysis: People > Tools
"If your improvement project fails, it won’t be because you don’t know enough math; it will be because you don’t know enough about people.”
About Me
- Dr. MMV
- I am the founder of University Training Partners, a company that designs and delivers online Lean Six Sigma and statistics training. In this blog, I hope to share my thoughts on statistics, quality assurance, and training, drawing from my 15 years of work experience in the field, eight years as a tenured professor teaching graduate statistics classes and ten years as a full-time training developer.
Showing posts with label Six Sigma. Show all posts
Showing posts with label Six Sigma. Show all posts
Thursday, January 14, 2021
Wednesday, January 27, 2016
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?
© 2016 Mary McShane-Vaughn
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
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