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As statistical methods become more embedded in everyday organizational quality improvement efforts, I find that a key concept is often woefully misunderstood, if it is even taught at all. W. Edwards Deming distinguished between two types of statistical study, which he called “enumerative” and “analytic.”
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The key need in quality improvement is that statistics should relate to reality, which then lays the foundation for a theory of using statistics (analytic). Whether you realize it or not, the perspective from which virtually all college courses and many belt courses are taught is population-based (enumerative), its purpose is estimation.
In a real-world environment, this becomes questionable at best because everyday processes are usually not static populations. Deming was emphatic that the purpose of statistics in improvement is prediction; the question becomes, “What other knowledge beyond probability theory is needed to form a basis for action in the real world?”
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Prediction is the Problem
Good article Davis. Prediction is central to Deming's idea of Analytic Studies in statistics. The following is from the Forward to Quality Improvement through Planned Experimentation by Moen, Nolan and Provost and written by Deming in 1990:
“…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
Static vs. Dynamic Population Analysis
This is an important issue in the construction materials industry as well where the 'population' is being consumed and replenished almost as fast as it can be sampled.
Static vs. Dynamic Population Analysis
This is an important issue for the construction materials industry as well; where the 'population' may be consumed and replenished as fast as it may be sampled.
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