There are lots of books on Design of Experiments (DOE) out there, all aimed at different audiences. Drawing on results and ideas from statistics, mathematics, and optimization, there’s a lot to learn—and no matter how much you’ve already read about it, it can be hard to know where to start.

We’ve amassed quite the bookshelf on DOE over the years, both physical and virtual. To help you narrow down your options, we’ve picked 7 of our favorites. We tell you a bit about why we love each of them, and who they would be most suitable for.

*Want something to get into straight away? Don’t forget our ebook, **A Biologist’s Guide To Design of Experiments**, our DOE training course where we teach you the fundamentals in 6 minutes a day, or our **DOE Masterclass webinar series**.*

## 7 Design of Experiments (DOE) books we love, and why

### 1. *DOE Simplified: Practical Tools for Effective Experimentation* (Anderson & Whitcomb)

**Why we love it: **

- Simplifies the statistical background that you need for DOE
- Backs up everything with real experimental examples
- Readable and accessible style

**Suitable for: **Both those with some knowledge of DOE and** **experienced practitioners

*DOE Simplified* is best for those looking to get a handle on DOE. It’s also the book that got our CSO Markus Gershater into DOE in the first place!

It was written by 2 members of Stat-Ease, who make Design Expert. Given the focus and success that Design Expert has always had on making DOE accessible, it makes a lot of sense that they would produce such an accessible book. Of course, the book is linked with the software, and readers are invited to download a free trial to play along at home.

That said, we were a little surprised that this DOE book isn’t as introductory as expected: It’s got a great deal of statistical detail in it. The “Simplified” part comes from trying to present all of the relevant statistical background in an accessible way. It also goes through things “properly”, building up the basic statistical foundations before moving on to the more DOE specific stuff.

Overall, we think this works really well. The style is very readable and accessible—and everything is backed up with specific DOE examples, both as tables showing calculations and, importantly, as real experimental examples.

Our biggest criticism is that it buries the lead. The initial flowchart guide to the book, for example, assumes a lot of DOE knowledge and focuses on details. While the book does have a very useful overview of the process as a whole, it’s all the way back in chapter 10. While building "from the ground up" like this is quite normal for textbooks of this kind (most of the books on this list do the same thing), for us it would have been even better to give more of an outline before getting into the details.

Still, the book has a lot to recommend itself both to those who are new to DOE, and to more experienced practitioners, as well.

### 2.* RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments* (Anderson & Whitcomb)

**Why we love it:**

- Good high-level overview of DOE
- Great context on each experiment type
- Readable and accessible style

**Suitable for: **DOE beginners and—purely for its discussion of the ins and outs of rotability in its later chapters—more seasoned readers

Following the success of *DOE Simplified*, *RSM Simplified *is the second book in the “...Simplified” series.

Overall, it’s an excellent read. For us, *RSM Simplified* is a better book to start with than *DOE Simplified*—as it’s often easier to understand the context of each type of experiment before grappling with the details involved in doing it. Instead of the statistical specifics like *DOE Simplified*, this book begins with a good overview of the RSM process.

It definitely gets more technical than *DOE Simplified* does in later chapters, but does a better job at giving a high-level overview of what’s going on, which is great for beginners. For more seasoned readers, we’d say that their in-depth discussion of rotatability is worth the book on its own.

### 3. *Formulation Simplified: Finding the Sweet Spot Through Design and Analysis of Experiments with Mixtures *(Anderson & Bezner)

**Why we love it:**

- Solid deepdive into mixture designs
- Great context of each experiment type
- Readable and accessible style

**Suitable for: **Anyone who needs to understand mixture designs in a lot of depth

*Formulation Simplified *(with Martin Bezner), the third in the “*...Simplified*” series, is a different beast altogether. Definitely non-introductory, this is a deepdive into mixture designs, a very important special case beyond the more general-purpose applications in the other 2 books.

Otherwise, it’s absolutely in line with the others. Just like the others in the “...Simplified” series, it makes technical matters more accessible.

However, unlike the other two, it’s most definitely special interest only. If you need to read this, you’ll know.

### 4. NIST Engineering Statistics Handbook (free!)

**Why we love it:**

- Particularly clear and concise intel on DOE and RSM
- Good context on origin of different methods and how they fit together
- Gets straight to the point
- Readable and accessible style
- Free

Suitable for: Stats lovers (equations don't frighten you!), as it's technically an engineering book

This is not strictly a DOE book because it covers a variety of statistical topics. But, if you’re comfortable with stats and not put off by equations this is a great first book to read.

It gets right to the point without doing the usual math-book thing of having 1 accessible page followed by a thousand pages of unexplained symbols. Chapters 4 (process modeling) and 5 (process improvement) are particularly useful for their introductory information on DOE and RSM.

A few of us at Synthace have read this either as a first or second book, and usually refer it to others. It works well as a first book because it does a lot to provide context around where the different methods come from, and how they fit together before getting into the details. It also gives some useful examples.

The downside is it *is* an engineering book, so all of the examples cover things like manufacturing and materials science. But they’re pretty simple and well-explained, so they still illustrate the ideas and methods well.

Even if you're not keen on equations, the explanatory sections of the chapters are worth a quick perusal. There's really no better concise explanation of the ideas behind DOE that we know of. Also, it's free! What's not to like?

### 5. *Statistics For Experimenters* (Box, Hunter & Hunter)

**Why we love it:**

- Excellent foundational text
- Covers the DOE fundamentals
*, as well as*tips and specialized topics - Exercises, examples, and experiments to try at home
- Easy to read, yet comprehensive
- Available in PDF format online

**Suitable for: **All levels

The classic book for DOE in many ways, and rightly so. This is a very readable, but rigorously comprehensive book. It covers a huge amount of ground, from statistical fundamentals through to more advanced, specialized topics like process control, designing for robustness, and evolutionary operation (a topic that Box also covered in another great book).

Unashamedly mathematical, this is a great read for anyone at almost any level. The material contains accessible explanations for beginners, with plenty of exercises, examples, and experiments to try at home. Plus, it includes lots of useful tidbits and rules of thumb as well as the specialized topics, making it worthy of experienced DOE practitioners.

An excellent foundational text, probably an alternative to *DOE* / *RSM Simplified* for an introduction. Though the book is starting to show its age a little. Firmly rooted in the more classical DOE methods, it’s missing treatments of optimal designs.

A couple of chapters worth special callouts include nonlinear designs and chapter 1, “Catalyzing the growth of knowledge”.

Easy enough to find online in PDF form, so you don’t need to pay the excruciating second-hand prices that it fetches in hardcopy.

### 6. *Optimal Design Of Experiments: A Case Study Approach* (Goos & Jones)

**Why we love it:**

- Excellent summary of optimal designs
- Carefully chosen case studies throughout
- Hard to put down

**Suitable for: **Anyone experienced with conventional DOE looking to get into using optimal designs

Until the more recent publication of *Design of Experiments: A Modern Approach * (up next on our list)*,* this was by far the most accessible and general introduction to using optimal designs.

Goos and Jones aimed to produce a book that would cater to both beginners and experts—a tough brief, as they admit, but to some extent successful.

Our guess is that they probably overestimate the statistical interest and experience of new practitioners somewhat. But as an introduction to people with the requisite statistics background, it is an excellent summary, with more than enough to get to grips with the subject.

The unifying theme of case studies is a good expository device. And while the mocked-up “consultancy” bits don’t entirely ring true as dialogue to our ‘inner ear’, the various ideas and questions they discuss are stimulating. The format works well for what could be a dry subject. Each case study is carefully chosen to highlight a specific, and often misunderstood, fact about design and analysis.

Overall, there are so many interesting and surprising details, we found it really hard to put down.

### 7. *Design of Experiments: A Modern Approach* (Jones & Montgomery)

**Why we love it:**

- Brings DOE into the twenty-first century
- Doesn’t bother with manual calculations
- Uses optimal designs as a jumping off point

**Suitable for: **People who’ve already read *DOE* *Simplified *or *RSM Simplified*

This one is well worth knowing about—the approach that the authors have taken is spot on.

All of the other DOE books on this list give a lot of emphasis to “classical” DOE designs and methods. They take you through everything you need to know. Then, they knuckle down and do your DOEs with a pen, some paper, and the appropriate reference materials, such as tables of designs.

This is a fantastic approach if you are traveling back in time to 1980 to do your DOEs. But here in 2022, we all have access to more than enough CPU power and great software to do things differently. A book that takes this basic fact as a starting point is incredibly welcome.

This has 2 important consequences. First, they don’t need to waste time on the details of how to calculate everything manually. Second, they use optimal designs as a jumping off point. This is a breath of fresh air, as none of the more accessible textbooks really give you much on how to approach using these. In our experience, optimal designs are often the only real choice for the experiments our colleagues want to do. We anticipate this will become an important book for users of DOE in biosciences.

Although this is intended as an introductory textbook, it's probably better read after *DOE Simplified *or *RSM Simplified* if you’re coming at it without a lot of statistical background; it goes into the details pretty quickly. However, the first chapter does score very highly as an overview of the main concerns of experimental design and is definitely a great introduction.

## You’re gonna need a bigger bookshelf

We’ve been through a handful of books, either entirely or mostly about DOE, including some of the best current resources alongside some classic choices. But of course, we’ve barely scratched the surface! There are plenty of other great books out there (we’re looking at you, Wu & Hamada!).

If you’re looking for something accessible that you can download immediately, we’d also recommend our ebook: A Biologist’s Guide To Design of Experiments. We wrote it with life science experimenters in mind. You can also watch the entirety of our DOE Masterclass webinar series, where we walk through the fundamentals of what DOE is, and how it can be applied in biological experiments.

## Michael "Sid" Sadowski, PhD

Michael Sadowski, aka Sid, is the Director of Scientific Software at Synthace, where he leads the company’s DOE product development. In his 10 years at the company he has consulted on dozens of DOE campaigns, many of which included aspects of QbD.