Category Archives: Design of Experiments

In Praise of Data-Driven Management (AKA “Why You Should be Skeptical of HiPPO’s”)

Posted on July 7, 2025 by Curiouscat

Guest post by Justin Hunter, originally published 18 August 2009. Jeff Fry recently linked to a fantastic webcast in Controlled Experiments To Test For Bugs In Our Mental Models. I would highly recommend it to anyone without any reservations. Ron Kohavi, of Microsoft Research does a superb job of using interesting real-world examples to explain the benefits of conducting small experiments with web site content and the advantages of making data-driven decisions. I firmly believe that the power of applied … Continue reading →

Posted in Data, Design of Experiments, IT, Management, Psychology, Quality tools, Software Development, Statistics | Tagged , , , ,

Profound Podcast: John Hunter – Curious Cat

Posted on February 18, 2022 by John Hunter

John Willis interviewed me for his Profound podcast series (listen to part one of the podcast, John Hunter – Curious Cat) John Hunter, Hurricane Ridge Trail, Olympic National Park This post provides links to more information on what we discussed in the podcast. Hopefully these links allow you to explore ideas that were mentioned in the podcast and that you would like to learn more about. Bill Hunter (my father): Quality In The Community One City’s Experience (Madison) – Quality … Continue reading →

Posted in Deming, Design of Experiments, Management | Tagged , , ,

Understanding Design of Experiments (DoE) in Protein Purification

Posted on August 4, 2016 by John Hunter

This webcast, from GE Life Sciences, seeks to provide an understanding Design of Experiments (DoE) using an example of protein purification. It begins with a good overview of the reason why multi-factorial experiments must be used while changing multiple factors at the same time in order to see interactions between factors. These interactions are completely missed by one-factor-at-a-time experiments. While it is a good introduction it might be a bit confusing if you are not familiar with multi-factorial designed experiments. … Continue reading →

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Design of Experiments: The Process of Discovery is Iterative

Posted on July 30, 2013 by John Hunter

This video is another excerpt on the design of experiments videos by George Box, see previous posts: Introduction to Fractional Factorial Designed Experiments and The Art of Discovery. This video looks at learning about experimental design using paper helicopters (the paper linked there may be of interest to you also). [the video is no longer available online] In this example a screening experiment was done first to find those factors that have the largest impact on results. Once the most … Continue reading →

Posted in Design of Experiments, Management, Process improvement, Quality tools, Statistics | Tagged , , , , , , | 3 Comments

George Box

Posted on March 28, 2013 by John Hunter

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 … Continue reading →

Posted in Data, Deming, Design of Experiments, Management, Manufacturing, Process improvement, Quality tools, quote, Respect, Six sigma, Statistics, Systems thinking, UK | Tagged , , , , , , , , , , , , , , | 15 Comments

Introductory Videos on Using Design of Experiments to Improve Results

Posted on June 18, 2012 by John Hunter

The video shows Stu Hunter discussing design of experiments in 1966. It might be a bit slow going at first but the full set of videos really does give you a quick overview of the many important aspects of design of experiments including factorial designed experiments, fractional factorial design, blocking and response surface design. It really is quite good, if you find the start too slow for you skip down to the second video and watch it. My guess is, … Continue reading →

Posted in Data, Design of Experiments, Science, Six sigma, Statistics | Tagged , , , , , , , , , | 6 Comments

One factor at a time (OFAT) Versus Factorial Designs

Posted on May 25, 2011 by John Hunter

Guest post by Bradley Jones Almost a hundred years ago R. A. Fisher‘s boss published an article espousing OFAT (one factor at a time). Fisher responded with an article of his own laying out his justification for factorial design. I admire the courage it took to contradict his boss in print! Fisher’s argument was mainly about efficiency – that you could learn as much about many factors as you learned about one in the same number of trials. Saving money … Continue reading →

Posted in Data, Design of Experiments, Popular, Quality tools, Science, Six sigma, Statistics | Tagged , , , , , , | 6 Comments

Factorial Designed Experiment Aim

Posted on April 25, 2011 by John Hunter

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks? When doing a DOE we select factors with levels to induce purposely changes in the response variable. Do we want the response variable to move within the specs of the customers? Or it doesn’t matter since we are learning about the process? The aim needs to consider what … Continue reading →

Posted in Design of Experiments, Management, Manufacturing, Process improvement, Quality tools, quote, Science, Six sigma, Statistics | Tagged , , , , , , , , , | 4 Comments

Highlights from Recent George Box Speech

Posted on November 16, 2009 by John Hunter

The JMP blog has posted some highlights from George Box’s presentation at Discovery 2009 [the broken link was removed] Infusing his entire presentation with humor and fascinating tales of his memories, Box focused on sequential design of experiments. He attributed much of what he knows about DOE [design of experiments] to Ronald A. Fisher. Box explained that Fisher couldn’t find the things he was looking for in his data, “and he was right. Even if he had had the fastest … Continue reading →

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YouTube Uses Multivariate Experiment To Improve Sign-ups 15%

Posted on August 17, 2009 by John Hunter

Google does a great job of using statistical and engineering principles to improve. It is amazing how slow we are to adopt new ideas but because we are it provides big advantages to companies like Google that use concepts like design of experiments, experimenting quickly and often… while others don’t. Look Inside a 1,024 Recipe Multivariate Experiment A few weeks ago, we ran one of the largest multivariate experiments ever: a 1,024 recipe experiment on 100% of our US-English homepage. … Continue reading →

Posted in Customer focus, Data, Design of Experiments, Google, IT, Management, Process improvement, Quality tools, quote, Science, Software Development, Statistics | Tagged , , , , , , , , , , , | 2 Comments

Combinatorial Testing for Software

Posted on March 26, 2009 by John Hunter

Combinatorial testing of software is very similar to the design of experiments work my father was involved in, and which I have a special interest in. Combinatorial testing looks at binary interaction effects (success or failure), since it is seeking to find bugs in software, while design of experiments captures the magnitude of interaction effects on performance. In the last several years my brother, Justin Hunter, has been working on using combinatorial testing to improve software development practices. He visited … Continue reading →

Posted in Design of Experiments, Process improvement, Quality tools, Software Development | Tagged , , , , , | 9 Comments

Statistics for Experimenters in Spanish

Posted on January 5, 2009 by John Hunter

Statistics for Experimenters, second edition, by George E. P. Box, J. Stuart Hunter and William G. Hunter (my father) is now available in Spanish. Read a bit more can find a bit more on the Spanish edition, in Spanish. Estadística para Investigadores Diseño, innovación y descubrimiento Segunda edición. Statistics for Experimenters – Second Edition: Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, … Continue reading →

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ASQ William Hunter Award 2008: Ronald Does

Posted on November 12, 2008 by John Hunter

The recipient of the 2008 William G. Hunter Award is Ronald Does. The Statistics Division of the American Society for Quality (ASQ) uses the attributes that characterize Bill Hunter’s (my father – John Hunter) career – consultant, educator for practitioners, communicator, and integrator of statistical thinking into other disciplines to decide the recipient. In his acceptance speech Ronald Does said: The first advice I received from my new colleagues was to read the book by Box, Hunter and Hunter. The … Continue reading →

Posted in Design of Experiments, Statistics | Tagged , , ,

Full and Fractional Factorial Test Design

Posted on July 29, 2008 by John Hunter

I am a fan of design of experiments as long time readers know (see posts on design of experiments). Continue reading →

Posted in Design of Experiments, Process improvement, Statistics | Tagged , , , | 3 Comments

Printer Product Development Using Design of Experiments

Posted on June 29, 2008 by John Hunter

MEMS development in less than half the time by Christopher N. Delametter, Eastman Kodak Company The traditional approach to optimizing a product or process using computer simulation is to evaluate the effects of one design parameter at a time. The problem with this approach is that interactions between design factors and second-order effects are likely to result in a locally optimized design that will provide far less performance than the global optimum. Kodak researchers use DOE to develop tests that … Continue reading →

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Improvement Through Designed Experiments

Posted on February 4, 2008 by John Hunter

The Rationale of Scientific Experimentation by John Dowd explains the value of designed experiments. Another difficulty in industrial experimentation is the existence of interactions. As has been stated, manufacturing processes are complex with many factors involved. In many processes these factors interact. This is particularly so for continuous processes such as plating or sputtering. Saying that the factors interact means more than that they are related to each other. It means that the effect of one (or more) factors on … Continue reading →

Posted in Data, Deming, Design of Experiments, Management, Science, Statistics