In Reply to Parent Post by Steve Prevette
He did invoke the Tchebysheff's Inequality, which holds for ANY distribution ...
From RANDOM data. Holds limited to no value for correlated or dependent distributions. All of Dr. Shewharts's work was done with many random cause distributions
- as in bark thickness. Real life processes do not always exhibit pure random "chance cause" variation, causing conditions where the simplistic application of standard deviations is inadequate.
It is still critical to understand the underlying causes of the variation to assure yourself that the data related to the process
is separated from any variation from measurement
, etc.- as they will mask the true process distribution with a tidy, yet inconvenient normal distribution.
Also, as you collect data, you will develop one distribution. However, it is a result of many sub-distributions contributed by the factors in the Total Variance Equation
. Shewhart states the more of these sub-components there are, the closer to normal the resulting distribution will be. True...masking the size and effect of the original causes and the appropriate control of each
- since you can control one
cause - not many
- with your adjustment
. Understanding the distribution of each of the contributors, as will as making the non-process contributors statistically insignificant, will put you in a better position to develop adequate control.
In fact, in some cases the more the resulting distribution appears normal, the less in control the process is!
Again, I hope you weren't looking for a plug and chug....