Statistical Engineering

Article Revised: March 27, 2019

In the movie “The Graduate,” the new graduate is told by a would-be mentor to remember only one word as he heads out into the world: Plastics. Times have changed. Hal Varian, the chief economist at Google says, ‘‘I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding.’’ Statistical methods are being used by a larger cross-section of people in a wider variety of industries than ever before. There are numerous reasons for this. Nearly everyone has what was once considered to be a supercomputer sitting on their desktop. Powerful statistical software is widely available, including popular packages like Minitab, JMP, SAS and SPSS, and extremely powerful free software. Oracle’s Crystal Ball software makes it possible to create a statistical distribution for any cell in a spreadsheet, making statistical simulation a snap. While becoming more sophisticated, the software is also becoming easier to use. Output is increasingly graphical and easier to explain to laypersons. The number of people trained in Lean Six Sigma methods is growing rapidly. There is an enormous amount of data saved in public and corporate data warehouses. The list goes on and on.

But perhaps the most important reason for the ballooning use of statistics is: it works.
If we take Aristotle’s logic as the historical starting point for rational analysis, and Galileo’s experimental method as the next major leap, then statistical methods might be viewed as the next step in applied analysis. Many problems don’t lend themselves to solution by pure logic nor by carefully planned and controlled experimentation. Most organizations, especially in the commercial sector, must deal with so many problems and such a dynamic external environment that they are forced to make quick decisions despite large uncertainty, then move on to the next problem. Statistical methods help these decision makers evaluate the evidence and make better decisions quickly. The tools and technology described in the first paragraph make this easier than ever before.
This situation is much more akin to engineering than it is to pure science. The approach has been termed “Statistical Engineering.” Authors Roger W. Hoerl and Ron Snee describe Statistical Engineering as follows:

“The statistical engineering discipline [is] the study of how to utilize the principles and techniques of statistical science for beneﬁt of humankind. From an operational perspective we deﬁne statistical engineering as the study of how to best utilize statistical concepts, methods, and tools and integrate them with information technology and other relevant sciences to generate improved results. In other words, engineers—statistical or otherwise—do not focus on advancement of the fundamental laws of science but rather how they might be best utilized for practical beneﬁt.

This definition goes beyond applied statistics. Statistical Engineering implies the application of statistics in a systematic framework that utilizes technology to create or improve products, processes and services that improve the lives of people. Disciplines such as Lean Six Sigma, Quality Engineering, Reliability Engineering, and others can be said to do this to some degree, but there are other ways to use Statistical Engineering, some quite unexpected. Billy Beane, general manager of MLB’s Oakland A’s and protagonist of Michael Lewis’s book Moneyball, had a problem: how to win in the Major Leagues with a budget that’s smaller than that of nearly every other team. Conventional wisdom long held that big name, highly athletic hitters and young pitchers with rocket arms were the ticket to success. But Beane and his staff, buoyed by massive amounts of carefully interpreted statistical data, believed that wins could be had by more affordable methods such as hitters with high on-base percentage and pitchers who get lots of ground outs. Given this information and a tight budget, Beane defied tradition and his own scouting department to build winning teams of young affordable players and inexpensive castoff veterans. Author Michael Lewis examines how in 2002 the Oakland Athletics achieved a spectacular winning record while having the smallest player payroll of any major league baseball team. Given the heavily publicized salaries of players for teams like the Boston Red Sox or New York Yankees, baseball insiders and fans assume that the biggest talents deserve and get the biggest salaries. However, argues author Michael Lewis, little-known numbers and statistics matter more.

Statistical Engineering is not limited to applied statistics, theoretical statistics have a place too. In a paper published in the April-June 2011 issue of the journal Quality Engineering author Philip R. Scinto offers this list of Statistical Engineering attributes:
• Meets high-level needs of an organization
• Work/study for the greater good
• Use of statistical concepts and tools
• Collaborative effort with other sciences
• Integrated with other sciences
• Documented protocol
• Activity continuous with sustainable life
• Improved results

It isn’t necessary that all items on the list be checked off, but the list is useful in evaluating whether an activity qualifies as Statistical Engineering or if it’s merely another clever use of statistics. The important thing isn’t the label we apply, but the improvement that can be achieved by properly using statistical methods along with science and technology to achieve a challenging goal.

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