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Eston Martz


Five More Insights From Noted Quality Leaders

Using statistics in innovative ways

Published: Wednesday, October 26, 2016 - 15:47

At last month’s Minitab Insights conference, experts from a wide range of industries offered some great lessons about how they use data analysis to improve business practices and solve a variety of problems. I shared five tips from quality leaders in yesterday’s column; here are five more.

Improve your skills while improving yourself

Everyone has personal goals they’d like to achieve, such as getting fit, changing a habit, or writing a book. Rod Toro, deployment leader at the financial brokerage firm Edward Jones, explained how challenging himself and his team to apply lean and Six Sigma tools to their personal goals has helped them better understand the underlying principles of quality improvement and personalized learning. They gained deeper insights and expanded their ability to apply quality methods in a variety of circumstances and situations.

We can’t claim the null hypothesis is true

Minitab technical training specialist Scott Kowalski reminded us that when we test a hypothesis with statistics, “failing to reject the null” doesn’t prove that the null hypothesis is true. It only means we don’t have enough evidence to reject it. We must keep this in mind when we interpret our results, and to be careful how we explain our findings to others. We also need to be sure our hypotheses are clearly stated, and that we’ve selected the appropriate test for our task.

Outliers can’t just be ignored, so you’d better investigate them

We’ve all seen them in our data, those troublesome observations that just don’t want to belong, lurking off in the margins, maybe with one or two other loners. It can be tempting to ignore or just delete those observations, but Larry Bartkus, senior distinguished engineer at Edwards Lifesciences, provided vivid illustrations of the drastic effect outliers can have on the results of an analysis. He also reminded us of the value in slowing down our assumptions, looking at the data in several ways, and trying to understand why data are the way they are.

Attribute agreement analysis is just one option

When we need to assess how well an attribute measurement system performs, attribute agreement analysis is the go-to method. But Thomas Rust, reliability engineer at Autoliv, demonstrated that many more options are available. In encouraging quality practitioners to “break the attribute paradigm,” Rust detailed four innovative ways to assess an attribute measurement system: measurement of an underlying variable, attribute measurement of a variable product, variable measurement of an attribute product, and attribute measurement of an attribute product.

Minitab users do great things

More than anything else, what we took away from Minitab Insights 2016 was an even greater appreciation for the people who are using our software in innovative ways—to increase the quality of the products we use every day, to raise the level of service we receive from businesses and organizations, to increase the efficiency and safety of our healthcare providers, and so much more.


About The Author

Eston Martz’s picture

Eston Martz

For Eston Martz, analyzing data is an extremely powerful tool that helps us understand the world—which is why statistics is central to quality improvement methods such as lean and Six Sigma. While working as a writer, Martz began to appreciate the beauty in a robust, thorough analysis and wanted to learn more. To the astonishment of his friends, he started a master’s degree in applied statistics. Since joining Minitab, Martz has learned that a lot of people feel the same way about statistics as he used to. That’s why he writes for Minitab’s blog: “I’ve overcome the fear of statistics and acquired a real passion for it,” says Martz. “And if I can learn to understand and apply statistics, so can you.”