Statistics: No Laughing Matter
by Matthew Barsalou, guest blogger
I told a friend about my interest in statistics, and he immediately told me a joke about broiled chicken and statistics.
The punch line involved my friend getting to eat all the chicken. Unfortunately, I forgot the rest of the joke. I can, however, assure you it was a very funny statistics-related joke.
People often make jokes when I mention my interest in statistics, and I don't think they make the jokes just because there are so many great statistics-related jokes available. There might be some good jokes about statistics, but I only know two and can only remember one.
I also don't think people make jokes about statistics because it is an inherently hilarious subject. It can be an interesting subject, but it is seldom a funny subject.
In fact, the subject can be deadly serious. In public health, statistics can be used to identify cancer clusters and to validate the effectiveness of medical tests. Failing to identify a cancer cluster or the presence of a disease or disorder in an individual could result in a medical problem going untreated.
There is also the opposite risk—falsely identifying a cancer cluster in a community or a disease in an individual. This would mean resources are wasted on healthy people, as well as the negative consequences which could result when a healthy person is given an incorrect terminal diagnosis. Being falsely diagnosed with terminal cancer is more than just statistics; it is a life-changing, personal tragedy for the person who was misdiagnosed.
For those of us using statistics in manufacturing, the consequences of improper use of statistics may not be as severe as in the medical field. It is still a serious subject. An improperly performed or simply flawed study could result in a product that angers formerly loyal customers, as the Coca-Cola Company learned when they introduced New Coke in 1985. The correct use of statistics can have series consequences for the safety of consumers and a company's financial well-being if a statistical study fails to identify a serious hazard in a product. There can also be financial consequences if a study incorrectly identifies a safety hazard where none exists.
Statistics are used in medical testing to determine both whether or not potential new medicines work, and to determine if they have unwanted side effects. Statistics are also used to determine if the benefits of some medicines outweigh the risks of using them. Here, an incorrect interpretation of statistical data could result in harming people with medicine that that was intended to help them.
The consequences of making a mistake when using statistics in business are not always severe; however, they could be. An improperly analyzed Student’s t-test may result in an implementing an expensive improvement that actually does not change anything about the product. Or it could result in the product unknowingly becoming less safe than it was before the improvement was implemented.
We may not even realize when the consequences of a statistical mistake could be severe.
For those of us who use statistics, but are not trained statisticians, fortunately there are resources available to help us in correctly selecting and applying statistical methods. The National Institute of Standards and Technology collaborated with the semiconductor industry's SEMATECH to produce a free online statistics handbook. Statistical practitioners can also attend training by universities, professional societies and industry. Practitioners can also attend training offered by Minitab.
To make up for the seriousness of this subject—as well as my inability to remember the statistics joke I mentioned at the start—I'll finish with this classic statistics joke from the Internet Joke Database:
Three statisticians went duck hunting. A duck flew out and the first statistician took a shot, the shot went a foot too high. The second statistician took his shot and the shot went a foot too low. The third statistician said, "We got it!"
Do you have a favorite statistics joke—or a cautionary tale?
About the Guest Blogger
Matthew Barsalou is a statistical problem resolution Master Black Belt at BorgWarner Turbo Systems Engineering GmbH. He is a Smarter Solutions certified Lean Six Sigma Master Black Belt, ASQ-certified Six Sigma Black Belt, quality engineer, and quality technician, and a TÜV-certified quality manager, quality management representative, and auditor. He has a bachelor of science in industrial sciences, a master of liberal studies with emphasis in international business, and has a master of science in business administration and engineering from the Wilhelm Büchner Hochschule in Darmstadt, Germany. He is author of the books Root Cause Analysis: A Step-By-Step Guide to Using the Right Tool at the Right Time, Statistics for Six Sigma Black Belts and The ASQ Pocket Guide to Statistics for Six Sigma Black Belts.