The quality and process improvement professions tend to rely heavily on statistical information. The very science of quality control can be said to have begun with Walter A. Shewhart’s development of the control chart and discovery of the concepts of special cause and common cause variation. But few would argue with the statement that there is a downside, and a dark side, to statistics. I hereby present a few examples of good, bad, and ugly statistical usage.

First the “good.” Statistical texts describe the advantages of using statistical methods at great length; I’ve contributed a page or two myself. Statistics can force us to look at data and facts, rather than relying on opinions and letting strong personalities force their beliefs on others. Statistical thinking uses data to separate variation from special causes and variation from common causes, thereby aiding decision making and learning. Statistics help us test our beliefs and to learn from experience. Statistics are the bridge between raw data and knowledge and understanding. They provide the means by which we can test our theoretical models of reality and learn from them. When statistical analysis fails to confirm our initial hypothesis, we are forced to reevaluate the hypothesis, which leads to improved understanding. Even if statistical analysis confirms our beliefs, we gain insight through the rigor it provides.

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