Ever wondered why quality control (QC) professionals check a sample instead of 100 percent of a shipment during inspection? Or maybe you’ve wondered why they use acceptance sampling, rather than simply inspecting an arbitrary quantity of goods, such as 10 or 20 percent?

I have daily conversations with manufacturer plant managers, quality managers, engineers, supervisors, and plant production workers about challenges when using statistical process control (SPC). Of the mistakes I witness in the application of SPC, I’d like to share the five most prevalent; they can be costly.

How early is too early to introduce quality into your everyday life? Have we missed out on improvement opportunities in our personal lives along our paths to achieving our career goals as quality professionals? These questions have me pondering how life could have been different for me growing up with a little more emphasis on data analysis for improvement.

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.