I mostly agree with you, Rolf (and Gunter). I would challenge your
joint use of the term "scientists". My quibble arises not regarding
biomedical practitioners (who may be irredeemable as a group) but
rather regarding physicists. At least in that domain, I believe those
domain experts are at least as likely, and possibly more so, to
understand issues relating to randomness as are statisticians.
Randomness has been theoretically embedded in the domain for the last
90 years or so.
--
David Winsemius, MD
--
On Mar 4, 2009, at 6:43 PM, Rolf Turner wrote:
On 5/03/2009, at 12:13 PM, Bert Gunter wrote:
"The purpose of the subject or discipline ``statistics'' is in
essence
to answer the question ``could the phenomenon we observed have arisen
simply by chance?'', or to quantify the *uncertainty* in any estimate
that we make of a quantity."
May I take strong issue with this characterization? It is far too
narrow and
constraining. We are scientists first and foremost. The most
important and
useful thing I do is to collaborate with other scientists to frame
good
questions, design good experiments and studies, and gain insight
into the
results of those experiments and studies (usually via graphical
displays,
for which R is superbly suited). Blessing data with P-values is
rarely of
much importance, and is often frankly irrelevant and even
misleading (but
that's another rant).
George Box said this much better than I: "The business of the
statistician
is to catalyze the scientific learning process."
This is much much more than you intimate.
I must respectfully disagree. Far be it from me to argue with
George Box,
but nevertheless ... it may be statisticians *business* to catalyze
the
scientific learning process, but that is the business of *any*
scientist.
What we bring to the process is our understanding of the essentials of
statistics, just as the chemist brings her understanding of the
essentials
of chemistry and the biologist her understanding of the essentials of
biology.
The essentials of statistics consist in answering the question of
``could
this phenomenon have arisen by chance?'' This is where we
contribute in a
way that other scientists do not. They don't understand
variability, the
poor dears. (Unless they have been well taught and thereby have
become
in part statisticians themselves.) They have a devastating tendency
to treat
an estimated regression line as *the* regression line, the truth.
And so on.
The *way* we address the question of ``could it have happened by
chance''
and the way we address the problem of quantifying variability is
indeed open
to a broad range of techniques including graphics.
Note that I did not say word one about p-values. The example I gave
was
a scientific question --- is there a difference in the home field
advantage
between the English Premier Division and the equivalent division or
league
in Italy? How much of a difference? You may wish to throw in a p-
value,
or you may not. You will probably wish to look at a confidence
interval.
You may wish to look at the question from the point of view of the
distribution
of (home) - (away) differences, in which case graphics will most
certainly
help. But it comes down to answering the basic question. If you
have no
ability to answer such questions you are not, or might as well not
be, a
statistician.
cheers,
Rolf Turner
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