What are the panel's recommendations for books on design of experiments?

Ideally, books should be still in print or available electronically, although that may not always be feasible. If you feel moved to add a few words on what's so good about the book that would be great too.

Also, aim for one book per answer so that voting can help sort the suggestions.

(Community Wiki, please edit the question if you can make it better!)

16 Answers 16

Montgomery's Design and Analysis of Experiments is a classic and highly regarded text:

If you are interested in experimental design in a particular field (eg. clinical trials) other more specialised texts may be appropriate.

  • 1
    I had a look at this, and it's perfectly fine but has a bit too much of the feel of an undergraduate text book for my tastes. Not that there's anything wrong with that... – walkytalky Aug 27 '10 at 9:44
  • 1
    Very nice book indeed! I've started long ago to reproduce his analysis (with Design Expert and SAS) using R, but never find time to finish it. If you like to check it out, aliquote.org/articles/tech/dae – chl Aug 27 '10 at 11:17
  • I would not recommend this, it contain errors. Many books mentioned in other answers are better! – kjetil b halvorsen Jan 16 '16 at 21:37

for me, the best book around is by George Box:

Statistics for Experimenters: Design, Innovation, and Discovery

of course the book by Maxwell and Delaney is also pretty good: Designing Experiments and Analyzing Data: A Model Comparison Perspective, Second Edition

I personally prefer the first, but they are both top quality. They are a little bit expensive, but you can definitely find a cheap earlier edition for sale.

  • Very expensive... – SmallChess Apr 10 '15 at 11:34

Ronald Fisher's The Design of Experiments (link is Wikipedia rather than Amazon since it is long out of print) is interesting for historical context. The book is often credited as founding the whole field, and certainly did a lot to promote things like blocking, randomisation and factorial design, though things have moved on a bit since.

As a period document it's quite fascinating, but it's also maddening. In the absence of a common terminology and notation, a lot of time is spent painstakingly explaining things in what now seems comically-stilted English. If you had to use it as a reference to look up how to calculate something you'd probably gnaw your own leg off. But the terribly polite hatchet job on some of Galton's analysis is entertaining.

(I know, I know -- how the readers of tomorrow will laugh at the archaisms of today's scientific literature...)

I am surprise no one mentioned: Statistical Design by George Casella

Google Books Link

There are many excellent books on design of experiments. These procedures apply generally and I do not think there are special designs specific to bakery applications. Here are a few of my favorites.

  1. Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition [Hardcover] George E. P. Box (Author) J. Stuart Hunter (Author), William G. Hunter (Author)

  2. Design and Analysis of Experiments [Hardcover] Douglas C. Montgomery (Author)

  3. Design of Experiments: An Introduction Based on Linear Models (Chapman & Hall/CRC Texts in Statistical Science) [Hardcover] Max Morris (Author)

  4. Design and Analysis of Experiments (Springer Texts in Statistics) [Hardcover] Angela M. Dean (Author), Daniel Voss (Author)

  5. Experiments: Planning, Analysis, and Optimization (Wiley Series in Probability and Statistics) [Hardcover] C. F. Jeff Wu (Author), Michael S. Hamada (Author)

  6. Statistical Design and Analysis of Experiments, with Applications to Engineering and Science [Hardcover] Robert L. Mason (Author), Richard F. Gunst (Author), James L. Hess (Author)

  7. Statistical Design and Analysis of Experiments (Classics in Applied Mathematics No 22. ) [Paperback] Peter W. M. John (Author)

Not published yet, but I'm impatient for Design and analysis of experiments with R

There are not enough books on DoE with R. I'm very reluctant to proprietary software, and R documentation is not always the best

  • I have been able to review this book. I need to borrow a copy again (now that I have a little more experience with R), but this was a fantastic resource in terms of depth and breadth of experimental designs. I also appreciated the decision tree of designed experiments. – Tavrock Mar 30 '17 at 15:44

Experiments: Planning, Analysis and Optimization by Wu & Hamada.

I'm only a couple of chapters in, so not yet in a position to recommend confidently, but so far it looks like a good graduate text, reasonably detailed, comprehensive and up-to-date. Has more of a "no nonsense" feel than the Montgomery.

Experimental Design for the Life Sciences, by Ruxton & Colegrave. Aimed primarily at undergraduates.

If you're interested in pharmaceutical trials, two books I recommend:

  1. Statistical Issues in Drug Development by Stephen Senn (Amazon link)
  2. Cross-over Trials in Clinical Research by Stephen Senn (Amazon link)

If your field is biology/ecology, a nice and well written text is "Experimental Design and Data Analysis for Biologists" of Quinn and Keough (amazon

the work done by Underwood is also very interesting to read:

Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance (amazon)

Not really a book but a gentle introduction on DoE in R: An R companion to Experimental Design.

The Design of Experiments: Statistical Principles for Practical Applications by Roger Mead. Examples are drawn from agriculture and biology, so probably most appropriate if you're interested in one of those fields. Rather expensive for a 600-page paperback but you can probably find it second-hand.

This book gives you a statistical perspective on experimental design:

Casella, G. (2008). Statistical Design. Springer.

Hands on DOE book

John Lawson has written two books.

  1. Design and Analysis of Experiments with SAS

  2. Design and Analysis of Experiments with R

One is for SAS users and another one for R users. Both the version are same in content and context, the only difference is the software used in the book. Second one which is for R users is more useful as R is open source. So this is more of an hands on DOE book. He has in fact developed a library around with name daewr

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