Recommended books on experiment design? 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!)
 A: 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.


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

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

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

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

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

*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) 

*Statistical Design and Analysis of Experiments (Classics in Applied Mathematics No 22. ) [Paperback] Peter W. M. John (Author) 
A: I am surprise no one mentioned: Statistical Design by George Casella
Google Books Link
A: 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
A: 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.
A: Experimental Design for the Life Sciences, by Ruxton & Colegrave. Aimed primarily at undergraduates.
A: If you're interested in pharmaceutical trials, two books I recommend:


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*Statistical Issues in Drug Development by Stephen Senn (Amazon link)

*Cross-over Trials in Clinical Research by Stephen Senn (Amazon link)

A: Not really a book but a gentle introduction on DoE in R: An R companion to Experimental Design.
A: 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)
A: 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.
A: Experimental Design in Biotechnology by Perry D. Haaland, ed Marcel Dekker. 
A: If you're in the social sciences:
Using Randomization in Development Economics Research: A Toolkit
A: I have recently reviewed a large collection of DoE books (17), with the following requirements:

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*Not a cookbook approach but geared towards understanding (hard requirement)

*Decently in-depth (hard requirement)

*Written with an understanding of the New Causal Revolution (nice-to-have)

*Uses Hasse diagrams to simplify understanding of variance structure (nice-to-have)

*Has problems or exercises, preferably with solutions, for self-study (must have problems, nice to have solutions)

*Does not use SAS (I hate SAS) - or at least has an alternative to SAS (hard requirement)

*Utilizes a depth-first approach rather than breadth-first (suits my learning style better - hard requirement)

*Geared towards upper-level undergraduate with prerequisites of mathematical statistics and linear models (hard requirement)

The books I reviewed were the following (only first author listed):

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*Kaltenbach, Statistical Design and Analysis of Biological Experiments

*Casella, Statistical Design

*Montgomery, Design and Analysis of Experiments, 4th Ed.

*Montgomery, Design and Analysis of Experiments, 10th Ed.

*Montgomery, Design of Experiments: A Modern Approach

*Oehlert, A First Course in Design and Analysis of Experiments

*Lawson, Design and Analysis of Experiments with R

*Fisher, The Design of Experiments

*Morris, Design of Experiments: An Introduction Based on Linear Models

*Bailey, Design of Comparative Experiments

*Wu, Experiments: Planning, Analysis, and Optimization

*Dean, Design and Analysis of Experiments

*Box, Statistics for Experimenters

*Maxwell, Designing Experiments and Analyzing Data: A Model Comparison Perspective

*Mead, Statistical Principles for the Design of Experiments

*Cobb, Introduction to Design and Analysis of Experiments

*Kuehl, Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd Ed.
I was unable to find any book that had all of the desired characteristics. Indeed, I have come to think that Requirement #3 is not satisfied anywhere. Requirement #4 is only true for three books in the list (Oehlert, Kaltenbach, and Bailey). Two books were too advanced (Casella and Morris), though Casella would likely be a terrific graduate-level text. Very few books had answers to problems, and quite a few had no problems at all (Fisher and Kaltenbach, with Bailey and Mead having too few problems). A number of books just presented a cookbook approach: here's the design and how you analyze it, with a very limited attempt at getting to the understanding (all the Montgomery books, Oehlert, Lawson, Kuehl). Maxwell was far too wordy, Cobb was too low-level and also very wordy. Wu was breadth-first.
I narrowed it down to two possibilities that looked promising: Dean and Box. I had to read the first few chapters of both to determine that Dean uses a depth-first approach, while Box has a breadth-first approach.
So I have landed on Dean, Voss, and Draguljic Design and Analysis of Experiments as my favored book for self-study, based on my requirements. It fails Requirements #3 and #4 and does not have solutions for problems. But it does well enough on the other requirements to be the best option.
Fisher is worth reading, but is not sufficient on its own to train you to be proficient, as there are no problems to work. He has very good explanations, and it would be a great supplement.
A: 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.
A: 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.
A: 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...)
A: This book gives you a statistical perspective on experimental design:
Casella, G. (2008). Statistical Design. Springer.
A: Hands on DOE book
John Lawson has written two books. 


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*Design and Analysis of Experiments with SAS

*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
A: A contemporary reference that I've found really useful is
"Randomization in Clinical Trials" by Rosenberger and Lachin
While the focus is on randomized trials and in-human studies, it covers many topics not previously covered in a nice, codified reference (group sequential designs, covariate adaptive designs, causality, etc.).
Lachin has been a trusted reference with a great deal of influence on FDA decision making over the years. The book has some very interesting applied examples, particularly the ECMO study, to demonstrate contemporary issues in trials (blinding, bias, crossover, etc.)
