[R] Quotes from BHH2e

Douglas Bates bates at stat.wisc.edu
Thu Dec 2 16:47:31 CET 2004

Yesterday I had the opportunity to attend a seminar by George Box where 
he discussed some of the ideas that will be incorporated in the second 
edition of Box, Hunter, and Hunter "Statistics for Experimenters" due 
out in a few months.

At the end of the presentation he distributed a list of quotes from the 
book and I felt that many of these would be appealing to members of this 
mailing list.

I refer those who want R-related content in messages to this group to 
the quote "Seek computer programs that allow you to do the thinking."

My thanks to Professor Box for giving me permission to forward these.


The following list of quotations may be used for a number of purposes.

You may wish to be reminded of some of the ideas in this book.

Your boss, who may not have time to read the whole book, can employ
them to understand the philosophy of what you are doing.

If you use the book to teach a course some quotes can be used as
topics for short essays.

  Among the factors to be considered there will usually be a vital few
  and a trivial many.  (J.M. Juran)

  A process should be routinely operated in an evolutionary mode so as
  to produce not only product but information on how to improve the

  Sometimes the only thing you can do with a poorly designed experiment
  is to try to find out what it died of.  (R.A. Fisher)

  The experimenter who believes that only one factor at a time should
  be varied is amply provided for by using a factorial experiment.

  If there were a probability of only p = 0.04 of finding a crock of
  gold behind the next tree, wouldn't you go and look?

  The democratization of Scientific method.

  Designing an experiment is like gambling with the devil: only a
  random strategy can defeat all his betting systems.  (R.A. Fisher)

  Seek computer programs that allow you to do the thinking.

  When the ratio of the largest to smallest observation is large you
  should question whether the data are being analyzed in the right
  metric (transformation) .

  Original data should be presented in a way that will preserve the
  evidence in the original data.  (W. A. Shewhart)

  You can see a lot by just looking.  (Yogi Berra)

  A computer should make both calculations and graphs.  Both sorts of
  output should be studied; each will contribute to understanding.
  (F.J. Anscombe)

  Murphy works hard to ensure that anything that can go wrong will go
  wrong. With an adequate system of process monitoring, therefore, more
  and more of the things that can go wrong will be corrected and more
  and more of Murphy's tricks can be permanently stymied.

  A useful type of time series model is a recipe for transforming
  serial data into white noise.

  When you see the credits roll at the end of a successful movie you
  realize there are many more things that must be attended to in
  addition to choosing a good script.  Similarly in running a
  successful experiment there are many more things that must be
  attended to in addition to choosing a good experimental design.

  Iterative inductive-deductive problem solving is geared to the
  structure of the human brain and is part of every day experience.

  What does what to what? How, with a minimum of effort, can you
  discover which factors do what to which responses?

  Only in exceptional circumstances, do you need to try to answer all
  questions with one experiment.

  Actions called for as a result of an experiment are of two kinds:
   1) "Cashing in" on new knowledge
   2) Using the new knowledge to look for further possibilities of

  The business of life, is to endeavor to find out what you don't know
  from what you do; that's what I called "guessing what was at the
  other side of the hill".  (Duke of Wellington)*

  The best time to plan an experiment is after you've done
  it. (R.A. Fisher)

  Every model is an approximation.

  It is the data that are real (they actually happened!)

  The model is a hypothetical conjecture that might or might not
  summarize and/or explain important features of the data.

  All models are wrong; some models are useful.

  Don't fall in love with a model.

  It is a capital mistake to theorize before one has data.  Sherlock
  Homes in "Scandal in Bohemia" (Conan Doyle)

  It is not unusual for a well-designed experiment to analyze itself.

  Correlation may have nothing to do with causation: beware the lurking

  The idea of a process in a perfect state of control contravenes the
  second law of thermodynamics: thus a state of control is an
  unrealizable and must be regarded as a purely theoretical concept.

  The design of experiments was invented by R.A. Fisher to make it
  possible to conduct valid experiments in an environment (agricultural
  trials) that was never in a state of control.

  To find out what happens when you change something it is necessary to
  change it.

  It's better to solve the right problem approximately than the wrong
  problem exactly.  (J.W. Tukey)

  Experiment and you'll see!

  Perfection is not possible it's always an approximation.

  Most often an experiment does not allow us to make a final decision
  but to see what's worth trying.

  "Block what you can and randomize what you can't" can approximately
  justify an analysis "as if" standard assumptions were true.

  The largest member of any group is large - but is it exceptionally

  Where there are three or four machines, one will be substantially
  better or worse than the others.  (Ellis Ott)

  That conclusions reached in one environment (say from lab
  experiments) will apply in a different environment (say the full
  scale process) is based not on statistical reasoning but on what
  Deming called "a leap of faith".  Statistical methods can reduce but
  not eliminate the necessary leap.

  Discovering the unexpected is more important than confirming the

  One must learn by doing the thing; for though you think you know it,
  you have no certainty until you try.  (Sophocles)

  We should not be afraid of discovering something.

  When running an experiment the safest assumption is that unless
  extraordinary precautions are taken it will be run incorrectly.

  Knowledge is power (Francis Bacon)

  Show me the data!

  With sequential assembly, designs can be build up so that the
  complexity of the design matches that of the problem.

  At any given stage, the current model helps us appreciate not only
  what is known but what else it may be important to find out.

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