Quality Progress - May 2014 - 64

ONE GOOD IDEA BY MATTHEW BARSALOU Mix It Up Combining exploratory data analysis and strong inference JOHN W. TUKEY of Bell Telephone Labo- The SI steps are: Suppose a quality engineer receives ratories and Princeton University devel- 1. Devise an alternative hypothesis. several customer complaints about a oped exploratory data analysis (EDA) as 2. Devise a crucial experiment that ex- mechanical link that has been failing. The a way to analyze data for generating ideas that could be confirmed later.1 This contrasts the University of Chicago biophysicist John R. Platt's concept of strong inference (SI), which uses the sci- cludes, if possible, other hypotheses. 3. Perform the experiment. Then, repeat quality engineer initiates a containment action until the problem is corrected. All the procedure with each hypothesis be- parts in inventory were checked and found ing modified.5 in spec. It is unclear what the root cause SI is more empirical than the methods of the failure is or how to check for it. The entific method and alternative hypothesis.2 used in EDA and lacks EDA's flexibility, quality engineer can use EDA to review the Tukey intended EDA to be used for gen- but it is well suited to supporting or reject- available data. Statistical process control erating ideas that could be evaluated later ing a tentative hypothesis. data, for example, can generate run charts using confirmatory data analysis. Platt's and measurement reports that can be SI offers a method of confirmatory data used to produce box-and-whisker plots of analysis that can be coupled with EDA. various production batches to determine if there were changes between batches. Exploratory data analysis These methods cannot deliver a de- Calling EDA a toolset may be misleading. finitive root cause, but they can generate Tukey clearly stated that EDA is "not a hypotheses that can be verified using the bundle of techniques." Rather, it is "an empirical methods of SI. If EDA results in attitude, a flexibility and a reliance on dis- the hypothesis that parts in some batches play."3 This attitude is supported by many are on the low side of the spec limit and tools, such as stem-and-leaf diagrams this may cause the failure, the hypothesis and box-and-whisker plots, which were can be empirically evaluated by placing presented in Tukey's book, Exploratory parts in simulated conditions to attempt to Data Analysis.4 Other useful EDA tools recreate the failure. QP include multi-vari charts, run charts and REFERENCES histograms. The objective of EDA is not to quickly A complete method zero in on "the truth." It generates ideas In Against Method: Outline of an An- and hypotheses that can be confirmed archistic Theory of Knowledge, Paul using SI. Using the quick-and-easy visual Feyerabend argues that anything goes is methods of EDA, a quality engineer or "the only principle that does not inhibit technician can quickly identify ideas for progress."6 However, EDA and SI together further investigation. offer flexibility in analyzing data and generating hypotheses, and they provide a Strong inference concrete and empirical method for evalu- Platt realized the SI approach was similar ating the resulting hypotheses. Although to inductive inference, which was devel- the concepts of EDA and SI are not new to oped by Francis Bacon and is taught to industry, combining EDA and SI provides students as the scientific method. The a structure for the use of well-known tools difference between the standard scientific such as the seven basic quality tools and method and SI is "systematic application." experimentation. 64 QP * www.qualityprogress.com 1. John W. Tukey, "We Need Both Exploratory and Confirmatory," The American Statistician, Vol. 34, No. 1, February 1980, pp. 23-25. 2. John R. Platt, "Strong Inference," Science, Vol. 146, No. 3,642, October 1964, pp. 347-353. 3. Tukey, "We Need Both Exploratory and Confirmatory," see reference 1. 4. John W. Tukey, Exploratory Data Analysis, Addison-Wesley Publishing Co., 1977. 5. Platt, "Strong Inference," see reference 2. 6. Paul Feyerabend, Against Method: Outline of an Anarchistic Theory of Knowledge, third edition, Verso, 1993. MATTHEW BARSALOU is an engineering quality expert at BorgWarner Turbo Systems Engineering GmbH. He earned a master's of science in business administration and engineering from Wilhelm B├╝chner Hochschule in Darmstadt, Germany, and a master's of liberal studies from Fort Hays State University in Kansas. He is an ASQ-certified quality technician, quality engineer and Six Sigma Black Belt. He is a senior member of ASQ, a QP technical reviewer and the ASQ country counselor for Germany. http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - May 2014

Up Front
LogOn
Expert Answers
Keeping Current
Mr. Pareto Head
Catching Fire
Making the Rounds
Plan of Attack
Virtual Voices
Innovation Imperative
Measure for Measure
Quality in the First Person
Career Corner
Standards Outlook
QP Toolbox
QP Reviews
One Good Idea

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