Understanding Probability is a Key Foundational Element for Six Sigma Statistics

      When I took my first two statistics classes as an undergraduate, I enjoyed both but never completely mastered the basics as well as I should have.  I memorized how to apply the various formulas for appropriate situations, did dozens and dozens of practice problems, and got through both classes by rote.  It wasn’t until years later when I took applied, inferential statistics training as part of my quality assurance duties, I realized where I dropped the ball the first time.  I never REALLY learned AND understood the basics of probability.
      Probability is a core foundational element of inferential statistics.  Some examples are probability distributions, confidence intervals, and that always challenging concept for new belts to master called hypothesis testing. I can’t think of an area where it doesn’t come into play. 

      I’m not going to go over all of them but one concept which is important for ‘statistical thinking’ is that of MECE or mutually exclusive, collectively exhaustive.  When two events are mutually exclusive, they both cannot occur at the same time.  If you add in collectively exhaustive, where the two events consider all possible outcomes, you have formulated an all-encompassing hypothesis.  Very powerful.

      If you are working with Yellow Belts, Green Belts, and even Black Belts who struggle with hypothesis testing and other similar concepts, recommend they go back and learn probability.  They should take whatever time and practice is necessary.  I’m very confident if they do, some of the fog around six sigma statistics will lift and the bright light of understanding will come shining through.