Issue: Volume 5, Number 10
Date: October 2005
From: Mark J. Anderson, Stat-Ease, Inc. (

Dear Experimenter,

Here's another set of frequently asked questions (FAQs) about doing design of experiments (DOE), plus alerts to timely information and free software updates. If you missed the previous DOE FAQ Alert, please click on the links at the bottom of this page. If you have a question that needs answering, click the Search tab and enter the key words. This finds not only answers from previous Alerts, but also other documents posted to the Stat-Ease web site.

Feel free to forward this newsletter to your colleagues. They can subscribe by going to If this newsletter prompts you to ask your own questions about DOE, please address them via mail

Here's an appetizer to get this Alert off to a good start: I first* saw this statistical puzzle detailed in "The curious incident of the dog in the night time" by Mark Haddon (paperback by Vintage Books (May, 2004)—see Evidently, self-proclaimed IQ record-holder Marilyn Vos Savant in a column called "Ask Marilyn" for Parade magazine laid out this problem, called the "Monty Hall" after the game show host for television's "Let's Make a Deal." Imagine that you can pick one of three doors behind which are two goats and one new automobile. Before it opens, the host shows that behind another door you see a goat. You then can keep the door you first chose, or switch to the other unopened one. What are your odds of winning the car if you make the switch? (Assume that, from the very start, the game-show host knows what is behind all three doors.) If your inclination is to say "50/50," do not feel bad—most people give this wrong answer. Consider that only 1/3rd of the time you will pick the car, thus 2/3rds of the time you win by picking the last unopened door after Monty reveals the one with the goat. In other words, you double your odds of winning by making the switch.

*(Also presented by the math whiz in the American television show "Numb3rs" in an episode from its first season in 2005. Evidently Hollywood has discovered the power of statistics as noted at

Here's what I cover in the body text of this DOE FAQ Alert (topics that delve into statistical detail are designated "Expert"):

1. Software Alert: Major new release of Design-Expert® program
2. FAQ: Statistical significance versus practical importance
3. Info Alert: Thought-provoking article on DOE and synergy
4. User feedback: Kudos to making DOE easy
5. Events alert: Fall Technical Conference in Saint Louis
6. Workshop alert: Experiment Design Made Easy in Anaheim, CA

PS. Quote for the month:
Einstein's observational study on the cause and effect relationship for holes in socks—with a surprising conclusion.


1. Software Alert: Major new release of Design-Expert® program

Stat-Ease announces a major new release—version 7 of Design-Expert software (DX7). For a free, fully-functional 45-day trial, click this link: Pricing for new licenses and upgrades can be seen at the Stat-Ease e-commerce site:

Those of you who’ve used previous versions will be impressed with the many improvements in V7, including:

  • Pareto chart of t-values of effects: Quickly see the vital few effects relative to the trivial many from two-level factorial experiments.
  • Min-Run Res IV (two-level factorial) designs for 5 to 50 factors: Screen main effects with maximum efficiency in terms of experimental runs.
  • On plots of effects simply lasso a box around the ones you want selected for your model: This is much easier than clicking each one with your mouse.
  • Central composite designs (CCD’s) now available for up to 30 factors and 8 blocks: This represents a significant expansion in RSM capability.
  • CCD’s based on Min-Run Res V fractional-factorial core:Take advantage of a much more efficient design for larger numbers of factors.
  • Box-Behnken designs expanded up to 21 factors: This popular response surface method (RSM) design previously was limited to 3,4,5,6,7,9 or 10 factors (note: 8 factors now possible).
  • Crosshairs window: Predict your response at any place in the response surface plot.
  • Full-color contour and 3D surface plots: Graduated or banded colorization adds life to reports and presentations.
  • Magnification feature: Incredible tool for expanding a mixture graph that is originally a small sliver and difficult to interpret.
  • Mixture-in-mixture designs: Develop sophisticated experiments for immiscible liquids or multilayer films involving separate formulations that may interact.
  • Add blocks D-optimally: This will be especially useful for mixture designs, which previously could not be blocked automatically.
  • Points on 3D graphs: See ‘lollipops’ protruding from surfaces where actual responses were collected.

This is only a partial list from the highlights that you can see listed in the Getting Started guide to Design-Expert V7 software at Page down to the Appendix of this document for many more new features in this landmark upgrade from Stat-Ease, Inc.


2. FAQ: Statistical significance versus practical importance

-----Original Question-----
From: Michigan

"If an experiment failed to show that a factor had a significant effect, is the experimenter justified in saying it is not a factor that needs to be included in the model? In medical study X, for example, let's say that bedside manner has a real impact, but not a significant one compared to error or the active drug. The practical outcome is that a factor that has an important but not yet detected effect may be dropped or thrifted out' (leading to poorer outcomes over the long run). However, in the literature it is frequently recommended that you can use the cheaper version, or discontinue use, of some chemical that is 'not significant.' What are your thoughts on this?"

This is a very brief response to a very weighty question.* Yes, it is very important to distinguish between an effect of a magnitude that is practically "important" versus one that is statistically "significant." In a design with too few runs to generate adequate power, effects may emerge that are important, but not significant. In this case, I say "if you feel it's real, then replicate," (the DOE equivalent of gangsta rapper Snoop Dog saying "If the ride is more fly, then you must buy.") If the design provides adequate power to detect even minimally important effects, then I'd agree in your second scenario that it would be lucrative to choose the more economical level of factors found insignificant, for example—the cheaper of two chemicals or the alternative of not using any, if this is one of the tested levels.

*(You readers are free to weigh in on this far too short answer. I will publish enlightening comments. For example, see the more conservative approach suggested by my colleague Pat. —Mark)

From: Pat Whitcomb—Stat-Ease Consultant
"An important consideration in selecting the ultimate level of a factor is that often we know from first principles and actual experience that this variable (for example, time or temperature) must affect the process. If the experimental results show such a factor to be insignificant, then this only indicates that over the tested range its effect was small relative to normal variation. By setting the factor at its mid-point the process becomes insensitive to variation in that factor, because its operational level falls within the middle of a plateau-like response surface. If the factor is set at either extreme it may be on the edge of the plateau and normal variation may send it onto a steep downward slope. Unless there is compelling reason to do otherwise (for example, economically), always set a non-significant factor at its the mid-point to achieve a robust process."

(Learn more about power by attending the three-day computer-intensive workshop "Experiment Design Made Easy." See for a course description. Link from this page to the course outline and schedule. Then, if you like, enroll online.)


3. Info Alert: Thought-provoking articles on DOE and synergy

The New York Society of Cosmetic Chemists published an intriguing two-part article titled "Desperately Seeking Synergy" by Joseph Albanese, which you can view at: (part 1) (part 2).
Joe, with whom I've established a correspondence, spices up this article on DOE with cartoons and thought-provoking diagrams and figures. With the spike in oil prices, it's easy to relate to his re-telling of John Cornell's gasoline blending example of synergy in mixtures: Wouldn't we all like to squeeze a few more miles per gallon from the incredibly costly fuel for our automobiles?


4. User feedback: Kudos to making DOE easy

From: John a PhD scientist from Maryland
"(You are welcome to pass along my comments in your alert, but please omit my surname and company. I would be happy to support your product in that manner.)

Dear Stat-Ease,

Recently, I purchased Design-Expert after examining the software through a demonstration trial. I am writing to share my enthusiasm for your software. The tutorial manual is easy to read, and the examples are very informative. I'm impressed at how "smart" the software is, allowing flexibility in correcting selections made in earlier steps and anticipating what the user needs/is looking for. Your software is leagues ahead of other DOE packages, for which I never did JMP for joy. I am now able to focus on my experimental design instead of laboring through the math myself (which I was doing!). I am truly looking forward to using Stat-Ease. Thank you!"

You are welcome! All of us at Stat-Ease really appreciate your compliments. For your information, I am the author of the User Guide tutorials. It is a lot of work, but knowing that these are put to such good use makes the tedium of writing them very worthwhile.


5. Events alert: Fall Technical Conference in Saint Louis

Pat Whitcomb will present at talk titled "Using a Pareto Chart to Select Effects for a Two-Level Factorial DOE" to the Fall Tech Conference (FTC) held in St. Louis, Missouri on October 20-21. It details a new method for using a Pareto chart of t-values* so that relative effect sizes get displayed properly, thus allowing the addition of t-limits that aid in the selection of the vital few that are likely to be statistically significant.

*(As noted in item #1, this is a new feature offered in version 7 of Stat-Ease software.)

Click for a list of appearances by Stat-Ease professionals. We hope to see you sometime in the near future!


6. Workshop alert: Experiment Design Made Easy in Anaheim, CA

The last Stat-Ease public workshop for 2005 will be a presentation of Experiment Design Made Easy on December 6-8 in Anaheim, California. This computer-intensive workshop on the basics of DOE has been extensively revised to make use of new features in version 7 of Design-Expert software. Nevertheless, it's main purpose is to educate on the principles of design and analysis of experiments, aided by the statistical calculations and graphical tools provided by the DX7 program.

See for schedule and site information on all Stat-Ease workshops open to the public. To enroll, click the "register online" link on our web site or call Stat-Ease at 1.612.378.9449. If spots remain available, bring along several colleagues and take advantage of quantity discounts in tuition, or consider bringing in an expert from Stat-Ease to teach a private class at your site. Call us to get a quote.


I hope you learned something from this issue. Address your general questions and comments to me at:




Mark J. Anderson, PE, CQE
Principal, Stat-Ease, Inc. (
2021 East Hennepin Avenue, Suite 480
Minneapolis, Minnesota 55413 USA

PS. Quote for the month—Einstein's observational study on the cause and effect relationship for holes in socks—with a surprising conclusion:

"When I was young, I found out that the big toe always ends up making a hole in the sock. So I stopped wearing socks."

—Albert Einstein

(Cited in "Age Doesn't Matter Unless You're a Cheese" by Kathryn and Ross Petras. I've also heard that it's good to be old, unless you are a banana. Mark)

Trademarks: Design-Ease, Design-Expert and Stat-Ease are registered trademarks of Stat-Ease, Inc.

Acknowledgements to contributors:
—Students of Stat-Ease training and users of Stat-Ease software
—Fellow Stat-Ease consultants Pat Whitcomb and Shari Kraber (see for resumes)
—Statistical advisor to Stat-Ease: Dr. Gary Oehlert (
—Stat-Ease programmers, especially Tryg Helseth (
—Heidi Hansel, Stat-Ease marketing director, and all the remaining staff


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