Hi Wirt, I completely agree with almost all of
what you (and David) wrote. Feynman is talking
about a real hypothesis that arose from a great
deal of thought and creativity...not one that
has been attached with baling wire, duct tape
and a little leftover Juicy Fruit to a pile of
data that happened to be sitting around.
That said, science is many things - 'a predictive
enterprise, not some form of mindless
after-the-fact exercise in number crunching.' -
fits under the umbrella but I don't think
captures the whole enterprise. Sequencing the
human genome was, in my opinion, a version of
mindless number crunching (although perhaps
somebody can put that effort in a hypothesis
testing context that I haven't thought of). I
think most people would be hard pressed to say
it wasn't science. In fact, there is an
emerging field of statistics (data mining) that
seems to be useful in developing scientific
hypotheses and is all about the 'mindless
after-the-fact exercise in number
crunching'. My feeling is that data can provide
hypotheses or test them. When it does the
first, it is a very useful part of science but
it is not predictive and it does not test
hypotheses (null, competing or otherwise). When
it does the latter it falls ito the category that Feynman was describing.
I think the reason we often get these trivial
tests of hypotheses is because there is this
sense that science is only about testing
hypotheses - therefore to do science I must test
a hypothesis...whether there is a meaningful one
or not. In my opinion, science can also just be
about looking for patterns that we can use to
suggest hypotheses. Hypotheses have to be
tested to be useful but the patterns we see in
nature (and those patterns are often less
distinct without number crunching)are almost
always the birthplace of hypotheses. Best.
Jeff H
-----Original Message-----
From: Wirt Atmar <[EMAIL PROTECTED]>
To: [email protected]
Date: Wed, 20 Feb 2008 12:03:54 -0700
Subject: [ECOLOG-L] Anderson's new book, "Model
Based Inference in the Life Sciences"
I just purchased David Anderson's new book, "Model Based Inference in the Life
Sciences: a primer on evidence," and although I've only had the opportunity to
read just the first two chapters, I wanted to write and express my enthusiasm
for both the book and especially its first chapter.
David and Ken Burnham once bought me lunch, and
because my loyalties are easily
purchased, I may be somewhat biased in my approach towards the book, but David
writes something very important in the first chapter that I have been mildly
railing against for sometime now too: the
uncritical overuse of null hypotheses
in ecology. Indeed, I believe this to be such an
important topic that I wish he
had extended the section for several more pages.
What he does write is this, in part:
"It is important to realize that null hypothesis testing was *not* what
Chamberlin wanted or advocated. We so often
conclude, essentially, 'We rejected
the null hypothesis that was uninteresting or
implausible in the first place, P
< 0.05.' Chamberlin wanted an *array* of *plausible* hypotheses derived and
subjected to careful evaluation. We often fail to fault the trivial null
hypotheses so often published in scientific journals. In most cases, the null
hypothesis is hardly plausible and this makes the study vacuous from the
outset...
"C.R. Rao (2004), the famous Indian
statistician, recently said it well, '...in
current practice of testing a null hypothesis,
we are asking the wrong question
and getting a confusing answer'" (2008, pp. 11-12).
This is so completely different than the extraordinarily successful approach
that has been adopted by physics.
In ecology, an experiment is most normally designed so its results may be
statistically tested against a null hypothesis.
In this procedure, data analysis
is primarily a posteriori process, but this is an intrinsically weak test
philosophically. In the end, you rarely understand more about the processes in
force than you did before you began. But the
analyses characteristic of physics
donât work that way.
In 1964, Richard Feynman, in a lecture to students at Cornell that's available
on YouTube, explained the standard procedure that has been adopted by
experimental physics in this manner:
"How would we look for a new law? In general we look for a new law by the
following process. First, we guess it.
(laughter) Then we... Don't laugh. That's
the damned truth. Then we compute the consequences of the guess... to see if
this is right, to see if this law we guessed is right, to see what it would
imply. And then we compare those computation results to nature. Or we say to
compare it to experiment, or to experience. Compare it directly with
observations to see if it works.
"If it disagrees with experiment, it's wrong. In that simple statement is the
key to science. It doesn't make a difference how beautiful your guess is. It
doesn't make a difference how smart you are, who
made the guess or what his name
is... (laughter) If it disagrees with experiment, it's wrong. That's all there
is to it."
-- http://www.youtube.com/watch?v=ozF5Cwbt6RY
In physics, the model comes first, not afterwards, and that small difference
underlies the whole of the success that physics has had in explaining the
mechanics of the world that surrounds us.
The entire array of plausible hypotheses that
were advocated by Chamberlin don't
all have to present during the first
experimental attempt at verification of the
first hypothesis; they can occur sequentially over a period of years.
As David continues, "We must encourage and
reward hard thinking. There must be a
premium on thinking, innovation, synthesis and creativity" (p. 12), and this
hard thinking must be done in advance of the
experiment. Science is a predictive
enterprise, not some form of mindless after-the-fact exercise in number
crunching.
Although expressed in a different format, David Anderson is saying the same
thing as Richard Feynman, and I very much congratulate him for it.
Wirt Atmar
