Currently browsing the Design of Experiments Category

Posts about design of experiments (DoE) - six sigma, (factorial designs, multivariate experiments) as used by statisticians since RA Fisher and now with six sigma.
We recommend Statistics for Experimenters. Articles on DoE: Teaching Engineers Experimental Design With a Paper Helicopter, Development of the Theory of Experimental Design by Sir R.A Fisher


Design of Experiments: The Process of Discovery is Iterative

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).

In this example a screening experiment was done first to find those factors that have the largest impact on results. Once the most important factors are determined more care can be put into studying those factors in greater detail.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician, and many of his other books.

The importance of keeping the scope (in dollars and time) of initial experiments down was emphasized in the video.

George Box: “Always remember the process of discovery is iterative. The results of each stage of investigation generating new questions to answered during the next.”

Soren Bisgaard and Conrad Fung also appear in this except of the video.

The end of the video includes several suggested resources including: Statistics for Experimenters, Out of the Crisis and The Scientific Context of Quality Improvement.

Related: Introductory Videos on Using Design of Experiments to Improve Results (with Stu Hunter)Why Use Designed Factorial Experiments?brainstormingWhat Can You Find Out From 8 and 16 Experimental Runs?

George Box

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.

photo of George EP Box

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

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Introductory Videos on Using Design of Experiments to Improve Results

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, for those unfamiliar with even the most cursory understanding of design of experiments, the discussion may start moving faster than you can absorb the information. One of the great things about video is you can just pause and give yourself a chance to catch up or repeat a part that you didn’t quite understand. You can also take a look at articles on design of experiments.

I believe design of experiments is an extremely powerful methodology of improvement that is greatly underutilized. Six sigma is the only management improvement program that emphasizes factorial designed experiments.

Related: One factor at a time (OFAT) Versus Factorial DesignsThe purpose of Factorial Designed Experiments

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One factor at a time (OFAT) Versus Factorial Designs

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 and effort is a powerful and positive motivator.

The most common argument I read against OFAT these days has to do with inability to detect interactions and the possibility of finding suboptimal factor settings at the end of the investigation. I admit to using these arguments myself in print.

I don’t think these arguments are as effective as Fisher’s original argument.

To play the devil’s advocate for a moment consider this thought experiment. You have to climb a hill that runs on a line going from southwest to northeast but you are only allowed to make steps that are due north or south or due east or west. Though you will have to make many zig zags you will eventually make it to the top. If you noted your altitude at each step, you would have enough data to fit a response surface.

Obviously this approach is very inefficient but it is not impossible. Don’t mistake my intent here. I am definitely not an advocate of OFAT. Rather I would like to find more convincing arguments to persuade experimenters to move to multi-factor design.

Related: The Purpose of Factorial Designed ExperimentsUsing Design of Experimentsarticles by R.A. Fisherarticles on using factorial design of experimentsDoes good experimental design require changing only one factor at a time (OFAT)?Statistics for Experimenters

Factorial Designed Experiment Aim

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 you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.
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Highlights from Recent George Box Speech

The JMP blog has posted some highlights from George Box’s presentation at Discovery 2009

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 available computer, he’d still be right,” said Box. Therefore, Fisher figured out how to study a number of factors at one time. And so, the beginnings of DOE.

Having worked and studied with many other famous statisticians and analytic thinkers, Box did not hesitate to share his characterizations of them. He told a story about Dr. Bill Hunter and how he required his students to run an experiment. Apparently a variety of subjects was studied [see 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments]

According to Box, the difficulty of getting DOE to take root lies in the fact that these mathematicians “can’t really get the fact that it’s not about proving a theorem, it’s about being curious about things. There aren’t enough people who will apply [DOE] as a way of finding things out. But maybe with JMP, things will change that way.”

George Box is a great mind and great person who I have had the privilege of knowing my whole life. My father took his class at Princeton, then followed George to the University of Wisconsin-Madison (where Dr. Box founded the statistics department and Dad received the first PhD). They worked together building the UW statistics department, writing Statistics for Experimenters and founding the Center for Quality and Productivity Improvement among many other things.

Statistics for Experimenters: Design, Innovation, and Discovery shows that the goal of design of experiments is to learn and refine your experiment based on the knowledge you gain and experiment again. It is a process of discovery. If done properly it is very similar to the PDSA cycle with the application of statistical tools to aid in determining the impact of various factors under study.

Related: Box on QualityGeorge Box Quotationsposts on design of experimentsUsing Design of Experiments

YouTube Uses Multivariate Experiment To Improve Sign-ups 15%

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. Utilizing Google Website Optimizer, we made small changes to three sections on our homepage (see below), with the goal of increasing the number of people who signed up for an account. The results were impressive: the new page performed 15.7% better than the original, resulting in thousands more sign-ups and personalized views to the homepage every day.

While we could have hypothesized which elements result in greater conversions (for example, the color red is more eye-catching), multivariate testing reveals and proves the combinatorial impact of different configurations. Running tests like this also help guide our design process: instead of relying on our own ideas and intuition, you have a big part in steering us in the right direction. In fact, we plan on incorporating many of these elements in future evolutions of our homepage.

via: @hexawiseMy brother has created a software application to provide much better test coverage with far fewer tests using the same factorial designed experiments ideas my father worked with decades ago (and yet still far to few people use).

Related: Combinatorial Testing for SoftwareStatistics for ExperimentersGoogle’s Website Optimizer allows for multivariate testing of your website.Using Design of Experiments

Combinatorial Testing for Software

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 me this week and we discussed the potential value of increasing the adoption of combinatorial testing, which is similar to the value of increasing the adoption of the use of design of experiments: both offer great opportunities for large improvements in current practices.

Automated Combinatorial Testing for Software

Software developers frequently encounter failures that occur only as the result of an interaction between two components. Testers often use pairwise testing – all pairs of parameter values – to detect such interactions. Combinatorial testing beyond pairwise is rarely used because good algorithms for higher strength combinations (e.g., 4-way or more) have not been available, but empirical evidence shows that some errors are triggered only by the interaction of three, four, or more parameters

Practical Combinatorial Testing: Beyond Pairwise by Rick Kuhn, US National Institute of Standards and Technology; Yu Lei, University of Texas, Arlington; and Raghu Kacker, US National Institute of Standards and Technology.

the detection rate increased rapidly with interaction strength. Within the NASA database application, for example, 67 percent of the failures were triggered by only a single parameter value, 93 percent by two-way combinations, and 98 percent by three-way combinations.2 The detection-rate curves for the other applications studied are similar, reaching 100 percent detection with four- to six-way interactions.
These results are not conclusive, but they suggest that the degree of interaction involved in faults is relatively low, even though pairwise testing is insufficient. Testing all four- to six-way combinations might therefore provide reasonably high assurance.

Related: Future Directions for Agile ManagementThe Defect Black MarketMetrics and Software DevelopmentFull and Fractional Factorial Test DesignGoogle Website Optimizer

Statistics for Experimenters in Spanish

book cover of Estadística para Investigadores

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, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.

* Graphical Analysis of Variance
* Computer Analysis of Complex Designs
* Simplification by transformation
* Hands-on experimentation using Response Service Methods
* Further development of robust product and process design using split plot arrangements and minimization of error transmission
* Introduction to Process Control, Forecasting and Time Series

Book available via Editorial Reverte

Related: Statistics for Experimenters ReviewCorrelation is Not CausationStatistics for Experimenters Dataposts on design of experiments

ASQ William Hunter Award 2008: Ronald Does

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 reason was clear. Because I was not familiar with industrial statistics I had to learn this from the authors who were really practicing statisticians. It took them years to write this landmark book.

For the past 15 years I have been the managing director of the Institute for Business and Industrial Statistics. This is a consultancy firm owned by the University of Amsterdam. The interaction between scientific research and the application of quality technology via our consultancy work is the core operating principle of the institute. This is reflected in the type of people that work for the institute, all of whom are young professionals having strong ambitions in both the academic world and in business and industry.

The kickoff conference attracted approximately 80 statisticians and statistical practitioners from all over Europe. ENBIS was officially founded in June 2001 as “an autonomous Society having as its objective the development and improvement of statistical methods, and their application, throughout Europe, all this in the widest sense of the words” Since the first meeting membership has grown to about 1300 from nearly all European countries.

Related: 2007 William G. Hunter AwardThe Importance of Management ImprovementResources on using statistical thinking to improve management

Full and Fractional Factorial Test Design

An Essential Primer on Full and Fractional Factorial Test Design

Since full factorial gathers additional data, it reveals all possible interactions, but as seen by the numbers above, there is a trade-off. More data equals more information but more data also equals a longer test duration. The minimum data requirements for full factorial are very high since you are showing every experiment.

Even if you are using full factorial to get the same amount of information as a fractional factorial test, it will take more time since you need more data to see statistically relevant differences between the many experiments. You might be wondering how fractional factorial can be accurate if interactions are possible?

Random interactions of high relevance are very rare, especially when looking for interactions of more than 2 factors. You really need to design tests where you look for meaningful interactions that are based on true business requirements rather than hoping for a random and low influence interaction between a red button, a hero shot and a headline.

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

Some good resources for more on the topics discussed above: What Can You Find Out From 8 and 16 Experimental Runs? by George Box – Statistics for ExperimentersDesign of Experiments in Advertising.

Related: Google Website Optimizerfactorial experiment articlesUsing Design of ExperimentsMarketers Are Embracing Statistical Design of Experiments

Printer Product Development Using Design of Experiments

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 examine first-order, second-order, and multiple factor effects simultaneously with relatively few simulation runs. The result is that the analyst can iterate to a globally optimized design with a far higher level of certainty and in much less time than the traditional approach.

By using DOE to drive CFD, Kodak researchers were able to optimize the design of the printhead in considerably less time than competitors. The advantages of simulation were especially apparent late in the project when researchers discovered a more optimal ink formulation for one of the colors.

Related: Design of Experiments articlesUsing Design of ExperimentsStatistics for ExperimentersWhy Use Designed Factorial Experiments?Kodak Debuts Printers With Inexpensive Cartridges

Improvement Through Designed Experiments

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 the response variable(s) changes when one (or more) other factor(s) changes its value.

In order to detect interactions and understand the nature of their effects it is necessary to combine the interacting factors into the same experimental runs. The problem is not necessarily knowing in advance if the interactions exist. Sometimes they are predictable with theory. Sometimes they are discovered when the process behaves ‘strangely’.

In addition to their efficiency, factorial designs also offer the only method of detecting interactions through experimentation. Because numerous factors can be combined in the same series of experimental runs, the interactions can be detected and the nature of their effects can be evaluated when they are present.

The paper also explains analytic and enumerative studies. Dr. Deming stressed the importance of understanding the distinction between the two.

Related: management improvement articlesDesign of Experiments articlesStatistics for Experimenterssearch statistical management improvement sitesUsing Design of Experiments

Design of Experiments in Operational Testing

Edwards looks toward future of testing

We are fielding a Design of Experiments concept to ensure we conduct the right amount of testing — not too much or too little, but just right. We will field this approach in phases as we must train our people and put the right tools in place. However, it is already showing great promise.

In a recent Benefield Anechoic Facility test, the 412th Electronic Warfare Group used Design of Experiments methodology to cut a two-month program to three weeks. This schedule reduction translated directly into savings and helped reduce the concept-to-fielding cycle time while still ensuring the system was thoroughly tested. While building these capabilities is critical, the most critical piece of the puzzle is our people. We must continue to develop engineers, pilots, navigators, program managers and maintainers to test these systems and “find stuff so the warfighter doesn’t.”

It is hard to tell if they really are using Design of Experiments or just using the term but it seem possible they are really using it. As I have said a number of times it is a powerful and under-utilized tool for improvement. Related: Using Design of Experimentsdesign of experiments articlesposts on public sector managementWhy Use Designed Factorial Experiments?

2007 William G. Hunter Award

T.N. Goh received ASQ Statistics Division’s 2007 William G. Hunter Award. He sent me this email:

You may not realize that 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.

Also, statistical design of experiments has assumed an increasingly important role in performance improvement and optimization in the face of constrained resources, again something always in the minds of engineers, managers and business leaders. From time to time there are others who package statistical tools under labels Bill might not even have seen himself, such as “Design for Six Sigma“, but the underlying idea is still the same: recognize the existence of variation, and the earlier you anticipate it and do something about it, the better off you will be in the end.

Bill’s zeal in spreading the message and sharing his knowledge and expertise with people in other parts of the world is well known; I would even say that he had recognized that “the world is flat” way before the likes of Tom Friedman discovered the reality of globalization!

So that’s to share my thoughts with you, having being honored by the Bill Hunter award. I am copying this to Stu, also to Doug who chairs the committee for this award. I reality enjoy the professional association and friendship with you all.

I had not realized Dad was helping set up the first school of engineering in Singapore. This is the kind of thing I mentioned in, The Importance of Management Improvement, where I mention people telling me the positive impact Dad had on their lives.

Related: Curious Cat Science and Engineering BlogStatistics for ExperimentersSingapore Research Fellowship

Six Sigma in Software Development

Six Sigma makes inroads in software development organizations

“A lot of big companies are developing their own software engineering variance of Six Sigma training,” said Siviy, “putting software-specific examples into the normal Six Sigma curriculum.” However, she said, it’s early in the adoption curve. “In the software world there is a real lack of case studies that show applications of Six Sigma in software engineering,” she said. And those that use Six Sigma in software are often reluctant to share examples because they consider it a competitive advantage.

Still, Siviy said, “At a lot of software conferences now you see a sprinkling of presentations that somehow touch on Six Sigma or Lean, and the quality and depth of questions have evolved tremendously. In general, and not just in Six Sigma, as the [software] industry matures you see a wave of interest in measurement and analytical techniques.”

McKesson is a prime example. “Measurement is key,” Childers said. “What you can’t or don’t measure, you don’t know.”

A couple points. First, you can know what you don’t measure. Do you know your parents? Do you measure them? Manage what you can’t measure.

The Software Engineering Institute at Carnegie Mellon University has great materials. There is a danger in using those materials to become overly bureaucratic but the material was developed out of an excellent understanding of quality management (way back when that was the way this stuff was referred to). David Anderson provides some good insights, see: Stretching Agile to fit CMMI Level 3

Design of experiments is very suited to testing software: Planning Efficient Software TestsDesign Of Experiment For Software Testing.

six sigma does seem to foster a lack of sharing; which is a shame.

Related: six sigma articles and linkssix sigma postssoftware development postsdesign of experiments articles

Mistakes in Experimental Design and Interpretation

Mistakes in Experimental Design and Interpretation

Humans are very good at detecting patterns, but rather poor at detecting randomness. We expect random incidents of cancer to be spread homogeneously, when in fact true randomness results in random clusters, not homogeneity. It is a mistake for an experiment to consider a pool of 47,000 possibilities, and then only report on the 7 cases that seem interesting.

A proper experiment states its hypothesis before gathering evidence and then puts the hypothesis to the test. Remember when you did your seventh grade science fair experiment: you made up a hypothesis first (“Hamsters will get fatter from eating Lucky Charms than Wheaties”) and then did the experiment to confirm or refute the hypothesis. You can’t just make up a hypothesis after the fact to fit the data.

This is an excellent article discussing very common errors in how people use data. We have tendencies that lead us to draw faulty conclusions from data. Given that it is important to understand what common mistakes are made to help us counter the natural tendencies.

Related: Seeing Patterns Where None ExistsIllusions, Optical and OtherUnderstanding DataDangers of Forgetting the Proxy Nature of DataHow to Deal with False Research Findingsdescriptive “theory” and normative theory

Google Website Optimizer

Google’s Website Optimizer allows for multivariate testing of your website.

Website Optimizer, Google’s free multivariate testing application, helps online marketers increase visitor conversion rates and overall visitor satisfaction by continually testing different combinations of site content (text and images).

Rather than sitting in a room and arguing over what will work better, you can save time and eliminate the guesswork by simply letting your visitors tell you what works best. We’ll guide you through the process of designing and implementing your first experiment. Start optimizing your most important web pages and see detailed reports within hours.

Google provides an online slide show with audio (a good example of one way to share online information sharing in my opinion). This tool seems to have limited experimental options to what is on the page (it does not appear, for example, that one variable could be current customer v. new visitor…). Still it looks like an very easy way to do some simple multi-factorial experiments. Google offers a list of partners for those interested in consulting and more advanced features (and for those experts reading this you can apply to be a partner).

Related: Design of experiments postsarticles on multi-factorial experimentationGoogle: Experiment Quickly and OftenData Based Decision Making at Google
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Experiment and Learn

Experimenting with milkshakes?

I have been on a mission to convince firms to do simple experiments that will give them feedback regarding the decisions that they make. Just as with people (as Anders Ericsson studies), firms cannot learn with feedback. It turns out, however, that it is not easy for people in companies to see the wisdom in experiments.

Experiments are useful and underused. PDSA and design of experiments are two concepts that aid in experimenting successfully.

Related: Google: Experiment Quickly and OftenWhy Use Designed Factorial Experiments?Using Design of Experimentstheory of knowledge

Scientific Thinking – the Modern Way

“Scientific thinking” the modern way by Bill Harris:

What does this all mean? It simply means that Fisher’s designed experiments give us better and faster means to extract insight from tests on system dynamics models than the old one-factor-at-a-time approach.

I thank Deb Schenk, then (and perhaps now) statistician at Hewlett-Packard Company, for teaching me and others about the design of experiments using Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building back in 1981-82.

I admit to a bit of bias, in seeing my father’s book (Statistics for Experimenters 2nd edition was published last year by the way), referenced but Bill Harris is exactly right in the power of design of experiments. The most recent post discusses Ackoff’s excellent f-Laws and a previous post discusses Deming (titled, It’s the process) so I couldn’t resist adding a post myself.

Related: design of experiments postsAckoff’s New Book: Management f-Laws

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