Great job Don! A contribution that cannot be made too often! Philip Crosby once told me: "If you turn to the local college for help on "Quality" the local professor will do a literature search and start teaching the stuff that got you into trouble in the first place." As Shewhart noted, things in nature are inherently stable, however, made man processes are inherently unstable. A major advancement for Minitab and other software would tell the student to first plot the data on a run chart, or better, a control chart before doing anything else. Unfortunately, many are taught to head down the wrong path with techniques not suited to the unstable environment for which they are applied. Deming does a good job of laying this out in the Forward to Quality Improvement through Planned Experimentation by Moen, Nolan and Provost:

...Why does anyone make a comparison of two methods, two treatments, two processes, or two materials? Why does anyone carry out a test or an experiment? The answer is to predict—to predict whether one of the methods or materials tested will in the future, under a specified range of conditions, performs better than the other one.

Prediction is the problem, whether we are talking about applied science, research and development, engineering, or management in industry, education, or government. The question is, What do the data tell us? How do they help us to predict?

Unfortunately, the statistical methods in textbooks and in the classroom do not tell the student that the problem in the use of data is prediction. What the student learns is how to calculate a variety of tests (t-test, F-test, chi-square, goodness of fit, etc.) in order to announce that the difference between the two methods or treatments is either significant or not significant. Unfortunately, such calculations are a mere formality. Significance or the lack of it provides no degree of belief—high, moderate, or low—about prediction of performance in the future, which is the only reason to carry out the comparison, test, or experiment in the first place.

Any symmetric function of a set of numbers almost always throws away a large portion of the information in the data. Thus, interchange of any two numbers in the calculation of the mean of a set of numbers, their variance, or their fourth moment does not change the mean, variance, or fourth moment. A statistical test is a symmetric function of the data.

In contrast, interchange of two points in a plot of points may make a big difference in the message that the data are trying to convey for prediction.

The plot of points conserves the information derived from the comparison or experiment. It is for this reason that the methods taught in this book are a major contribution to statistical methods as an aid to engineers, as well as to those in industry, education, or government who are trying to understand the meaning of figures derived from comparisons or experiments. The authors are to be commended for their contributions to statistical methods.

                                                                                      W. Edwards Deming

                                                                                      Washington, July 14, 1990

I hope I can remember that one next time I encounter it.