Guest post by Michael Betancourt.

I just finished Box, Hunter, and Hunter (Statistics for Experimenters) and I cannot praise it enough. There were multiple passages where I literally giggled. In fact I may have been a bit **too** enthusiastic about tagging quotes beyond “all models are wrong but some are useful” that I can’t share them all.

I wish someone had shared this with me when I was first learning statistics instead of the usual statistics textbooks that treat model development as an irrelevant detail. So many of the elements that make this book are extremely relevant to statistics today. Some examples:

- The perspective of learning from data only through the lens of the statistical model. The emphasis on sequential modeling, using previous fits to direct better models, and sequential experiments, using past fits to direct better targeted experiments.
- The fixation on checking model assumptions, especially with interpretable visual diagnostics that capture not only residuals but also meaningful scales of deviation. Proto visual predictive checks as I use them today.
- The distinction between empirical models and mechanistic models, and the treatment of empirical linear models as Taylor expansions of mechanistic models with covariates as _deviations_ around some nominal value. Those who have taken my course know how important I think this is.
- The emphasis that every model, even mechanistic models, are approximations and should be treated as such.
- The reframing of frequentist statistical tests as measures of signal to noise ratios.
- The importance of process drift and autocorrelation in data when experimental configurations are not or cannot be arbitrarily randomized.
- The diversity of examples and exercises using real data from real applications with detailed contexts, including units everywhere.

Really the only reason why I wouldn’t recommend this as an absolute must read is that the focus on linear models and use of frequentist methods does limit the relevance of the text to contemporary Bayesian applications a bit.

Texts like these make me even more frustrated by the desire to frame movements like data science as revolutions that give people the justification to ignore the accumulated knowledge of applied statisticians.

Academic statistics has no doubt largely withdrawn into theory with increasingly smaller overlap with applications, but there is so much relevant wisdom in older applied statistics texts like these that doesn’t need to be rediscovered just reframed in a contemporary context.

Oh, I forgot perhaps the best part! **BHH continuously emphasizes the importance of working with domain experts in the design and through the entire analysis with lots of anecdotal examples demonstrating how powerful that collaboration can be.**

I felt so much less alone every time they talked about experimental designs not being implemented properly andthe subtle effects that can have in the data, and serious effects in the resulting inferences, if not taken into account.

Michael Betancourt, PhD, Applied Statistician – long story short, I am a once and future physicist currently masquerading as a statistician in order to expose the secrets of inference that statisticians have long kept from scientists. More seriously, my research focuses on the development of robust statistical workflows, computational tools, and pedagogical resources that bridge statistical theory and practice and enable scientists to make the most out of their data.

Twitter: @betanalpha

Website: betanalpha

Patreon: Michael Betancourt

Related: Statistics for Experimenters, Second Edition – Statistics for Experimenters in Spanish – Statistics for Experimenters Review – Correlation is Not Causation

## George Box 1919 to 2013 – A Great Friend, Scientist and Statistician

Posted on March 30, 2013 Comments (2)

Reposted from my management blog.

I would most likely not exist if it were not for George Box. My father took a course from George while my father was a student at Princeton. George agreed to start the Statistics Department at the University of Wisconsin – Madison, and my father followed him to Madison, to be the first PhD student. Dad graduated, and the next year was a professor there, where he and George remained for the rest of their careers.

George died today, he was born in 1919. He recently completed An Accidental Statistician: The Life and Memories of George E. P. Box which is an excellent book that captures his great ability to tell stories. It is a wonderful read for anyone interested in statistics and management improvement or just great stories of an interesting life.

George Box by Brent Nicastro.

George Box was a fantastic statistician. I am not the person to judge, but from what I have read one of the handful of most important applied statisticians of the last 100 years. His contributions are enormous. Several well know statistical methods are known by his name, including:

George was elected a member of the American Academy of Arts and Sciences in 1974 and a Fellow of the Royal Society in 1979. He also served as president of the American Statistics Association in 1978. George is also an honorary member of ASQ.

George was a very kind, caring and fun person. He was a gifted storyteller and writer. He had the ability to present ideas so they were easy to comprehend and appreciate. While his writing was great, seeing him in person added so much more. Growing up I was able to enjoy his stories often, at our house or his. The last time I was in Madison, my brother and I visited with him and again listened to his marvelous stories about Carl Pearson, Ronald Fisher and so much more. He was one those special people that made you very happy whenever you were near him.

George Box, Stuart Hunter and Bill Hunter (my father) wrote what has become a classic text for experimenters in scientific and business circles, Statistics for Experimenters. I am biased but I think this is acknowledged as one of (if not the) most important books on design of experiments.

George also wrote other classic books: Time series analysis: Forecasting and control (1979, with Gwilym Jenkins) and Bayesian inference in statistical analysis. (1973, with George C. Tiao).

George Box and Bill Hunter co-founded the Center for Quality and Productivity Improvement at the University of Wisconsin-Madison in 1984. The Center develops, advances and communicates quality improvement methods and ideas.

The Box Medal for Outstanding Contributions to Industrial Statistics recognizes development and the application of statistical methods in European business and industry in his honor.

“All models are wrong but some are useful” is likely his most famous quote. More quotes By George Box

A few selected articles and reports by George Box

Related: It is not about proving a theorem it is about being curious about things – Box on Quality – Soren Bisgaard – Learning Design of Experiments with Paper Helicopters – Peter Scholtes

Categories: Engineering

Tags: books, commentary, data, design of experiments, experiment, John Hunter, learning, Madison, quote, Science, scientists, statistics