Blog posts on statistics

Posts selected fromManagement Blog - Engineering Blog - Investing Blog and other blogs

• Factfulness – An Extremely Valuable Book

  Data is extremely valuable in helping us make decisions and evaluating the effectiveness of policy. However it is critical to be careful. It is very easy to focus on meeting targets that seem sensible – increasing the number of hospital beds – but that lead to less effective policy.

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  The book relentlessly points out the great progress that has been made globally over the last 50 years and how that progress continues today and looks to be set to continue in the future. We have plenty of areas to work on improving but we should be aware of how much progress we have been making. As he points out frequently he has continually seen huge underestimation of the economic conditions in the world today. This book does a great job of presenting the real success we have achieved and the progress we can look forward to in the future.

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• Rethinking Statistics for Quality Control with George Box

  George Box shared a presentation on Rethinking Statistics for Quality Control at the 2008 Deming Institute Conference in Madison, Wisconsin.

  In the presentation George discusses how to look at data from a process. He mentions why it was so important to understand what Shewhart understood about process data: the order of the data is extremely important; which is why run charts and control (process behavior) charts are plotted in time order...

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• Data Can’t Lie

  Many people don’t understand the difference between being manipulated because they can’t understand what the data really says and data itself “lying” (which, of course, doesn’t even make sense). The same confusion can come in when someone just draws the wrong conclusion from the data that exists (and them blames the data for “lying” instead of themselves for drawing a faulty conclusion).

  The data can be wrong (and the data can even be made faulty intentionally by someone). Or someone can draw the wrong conclusion from data that is correct. But in neither case is the data lying. It is also common to believe the data means something other than what it does (therefore leading to a faulty conclusion).

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  If all those involved understand how to draw conclusions from data it is not easy to mislead them.

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• Data Based Blathering

  I am tired of seeing the American Customer Satisfaction Index (ACSI) promoted as if it were some encouragement for better management when all it seems to do to me is encourage superficial, non data based claims. And since it my blog I can rant if I feel like it.

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  They think a flat American Customer Satisfaction Index (ACSI) reading is going to lead to weak consumer spending? I doubt it. I really doubt it. What data, or theory is that based on? Jeez this whole thing just makes me crazy. Trying to use a index to promote the “importance of quality principles” (ASQ is one of the “sponsors” of this effort) and customer focus in this way – ARGH. It does the opposite – showing people how to misuse numbers. How to overreact to variation. How to compare one dot to another dot and make claims from those 2 dots. I am sure I will make mistakes in my statements but the ACSI has bugged me since it was started with the way it ignores sound quality practices and promotes the opposite of what people like Dr. Deming taught.

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• T.N. Goh received ASQ Statistics Division’s William G. Hunter Award

  I first met Bill 38 year ago, when he was in Singapore helping us set up the first school of engineering in the country. He persuaded me to go to the graduate school at UW-Madison and I daresay that’s the best advice I ever got in my whole career. Now when I come to think of it, what Bill stood for in his lifetime has not been, and never will be, out of date. He had advocated the use of statistical thinking and the systems approach, which if anything is even more critical today in handling issues such as global warming and government effectiveness...

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• Interview of Bill Hunter, Brian Joiner and Peter Scholtes on Better Management Practices

  That kind of experience could not have happened if management wasn’t willing to listen to the workers and wasn’t willing to say to the workers “you have brains and you have ideas and why don’t you go out and see if you can solve it and I will back you up. And that is what they did

  Bill on creating jobs people want to do:

  If they are going to work with the attitude that part of my job is to figure out how we can make things work better around here it adds another challenge to the job which makes the work more fun and more enjoyable. It all points in the same direction it seems to me. These methods do feed into making jobs more interesting and morale going up and the job being better.

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• Stratification and Systemic Thinking

  This is a great example of a positive special cause. How would you identify this? First you would have to stratify the data. It also shows that sometimes looking at the who is important (the problem is just that we far too often look at who instead of the system so at times some get the idea that it is not ok to stratify data based on who – it is just be careful because we often do that when it is not the right approach and we can get fooled by random variation into thinking there is a cause...

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• Prediction Markets with Google Employees

  Google’s prediction markets are reasonably efficient, but did exhibit four specific biases: an overpricing of favorites, short aversion, optimism, and an underpricing of extreme outcomes.

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• Interview of Bill Hunter by Peter Scholtes on Statistical Variability and Interactions

  For some processes it is enough to know a couple important variables and have an understanding of how they interact to impact results. Often though problems are created because the organization doesn’t learn enough about variables that can have a substantial impact on results and therefore feels blindsided by poor results. In some of those cases they were blindsided not by unforeseeable random factors but by variables they should have learned about. And then based on that knowledge designed their processes to take into account the potential impact of variations in that variable...

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• Managing with Control Charts

  ...if managers mistakenly tamper with a stable process, believing an occurrence is exceptional, they introduce an external cause, which destabilises it. Targets do the same thing.

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• Using Design of Experiments

  Design of Experiments can seem complicated but at the core it is fairly simple and powerful. By applying the proper techniques it allows you to gage the effect of several variables and, very importantly, the interactions of those variables with a small number of experiments (or tests or pilots)...

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• Data is Important and You Must Confirm What the Data Actually Says

  Without an appreciation for the gemba, where the data was collected, it is easy to be mislead by the data.

  The most common waste of effort in examining data is reacting to the expected variation of a system as if it is something special. We have discussed this in many previous posts, for example, We Need to Understand Variation to Manage Effectively. After that, I think there is a good chance of failure to appreciate what the data is, and is not, telling us based on mistaken assumptions about what the operational definitions were...

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• 2023 William G. Hunter Award to Dr. James Cochran

  ...It is also meaningful because of the distinguished, accomplished, highly regarded recipients who previously received this award. Dr. Hunter continues to have profound impact on our discipline and on my career - in service, in teaching, and in research. Dr. Hunter has influenced me by example, primarily through his vision and efforts to improve the lives of others by applying our discipline to complex and consequential problems.

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• George Box Webcast on Statistical Design in Quality Improvement

  There is great value in creating iterative processes with fast feedback to those attempting to design and improve. Box and Deming (with rapid turns of the PDSA cycle) and others promoted this 20, 30 and 40 years ago and now we get the same ideas tweaked for startups. The lean startup stuff is as closely related to Box’s ideas of experimentation as an iterative process as it is to anything else.   

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