All of us draw conclusions based on what we see happening around us. Often what we’re observing is a sample from some larger population of events, and we draw inferences based on the sample without even realizing it. If the sample we observe is not a representative one, our resulting judgments can be seriously flawed, potentially at considerable personal cost.

Genichi Taguchi is famous for his pioneering methods of robust quality engineering. One of the major contributions that he made to quality improvement methods is Taguchi designs.

Designed experiments were first used by agronomists during the last century. This method seemed highly theoretical at first, and was initially restricted to agronomy. Taguchi made the designed experiment approach more accessible to practitioners in the manufacturing industry.

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Although efforts have been made to create policies that support a bias-free workplace, there is still a considerable way to go toward achieving the gender equality that organizations are striving for. Due in part to a lack of clear measurement and transparency, many companies and industries as a whole are still lagging behind in the effort to have women and men equally represented, valued, and rewarded in the workplace.

Good measurements are like apple pie and motherhood. Who could ever be against having good measurements? Since we all want good measurements, it sounds reasonable when people are told to check out the quality of their measurement system before putting their data on a process behavior chart. Fortunately, this is simply one more bit of advice that is completely unnecessary.

Most leadership advice is based on anecdotal observation and basic common sense. Stanford Graduate School of Business professor Kathryn Shaw tried a different tack: data-driven analysis.

Having difficulty managing quality and quality-related data? You’re not alone. Many manufacturers struggle with this these issues due to paper-based or other disparate systems being used to track, manage, and report on quality events. Walk about a production room floor, and there’s a good chance you’ll see a few three-ring binders or folders stuffed with handwritten, quality-related worksheets and forms.

If you were among the 300 people who attended the first-ever Minitab Insights conference last month, you already know how powerful it was. Attendees learned how practitioners from a wide range of industries use data analysis to address a variety of problems, find solutions, and improve business practices. For those who weren’t there, here are five helpful, challenging, and thought-provoking ideas and suggestions that we heard during the event.

In all walks of life, being wrong can come with a penalty. It’s also true that, if you’re lucky, you sometimes get away with it without anybody being the wiser. To understand what this means in relation to the capability indexes C_p and C_pk, read on.

Part two of this four-part series on process capability concluded with Alan just about to meet Sarah for a second time. He thought he was making good progress with his analysis of Product 874 data until he was asked to assess process capability, even though it can’t be assessed for an unstable process.