From: hrubin@pop.stat.purdue.edu (Herman Rubin)
Newsgroups: sci.med
Subject: Misuse of the "scientific" method
Date: 10 Jan 93 18:59:44 GMT
Organization: Purdue University Statistics Department

In article <1993Jan7.142222.594@news.wesleyan.edu> RGINZBERG@eagle.wesleyan.edu (Ruth Ginzberg) writes:
>In <1iho59INNhtq@im4u.cs.utexas.edu> turpin@cs.utexas.edu writes:

>I ("Dr. Ruth") wrote:

>> > Could you please define what distinguishes prescientific 
>> > traditions from scientific traditions, preferably without 
>> > using the term 'science' redundantly?

>To which Russell responded

>> Of course.  In the last three centuries, there has developed an
>> extensive study of how to go about investigating general empirical
>> phenomena, what kinds of mistakes are likely in observation,
>> experimentation, and relating evidence to claims, how to minimize or
>> avoid these mistakes, and how to discuss and disseminate results with
>> an eye toward finding out the truth.  This has resulted in a better
>> understandings of such investigation and better techniques for doing
>> so, most of which did not exist previously.  Performing investigation
>> and research applying this better understanding and using these better
>> techniques is science. 

>I'm sure you don't mean to say that there was no science prior to the last
>three centuries.  You indicate pretty clearly in other parts of your article
>that you do not believe that.  Perhaps the problem is in your interesting and
>unusual concept of a 'definition'.  Let me be more clear:  Could you give me a
>*procedural* definition of science such that I can then look at practioner P
>within tradition T and determine correctly whether or not what s/he is doing is
>science?  After all, it is this distinction which is causing you and the
>various alt.advocates continually to be at odds with one another.  If you could
>agree on this procedural def., then applying it to particular practitioners of
>particular traditions should be merely a matter of factual accuracy.

The so-called "scientific method", which has produced, largely because of 
fortunate circumstances, great results in the physical sciences, moderate
results in the biological sciences, and mainly garbage in the social and
psychological sciences, essentially consists of trying to find "simple"
explanations to "fit" data, and only accepting something when it is "needed"
to produce an adequate fit.  At least this is what its advocates say; but it
is not what they do.  There is another step, and that is drawing conclusions
from the model.  

Now to give an example where this has been done with quite different results
by different people, consider vitamin C.  Linus Pauling has been castigated
in this group and elsewhere for coming up with his conclusions on the basis
of known information on vitamin C, mostly in non-primates.  On the other hand,
I have seen "establishment" medical advisors claim that megadoses of vitamin C
will produce kidney stones.  Now the experiments that one would want to carry
out to validate either are very unlikely to ever be done, and absent large
numbers of excess kidney stones in users of vitamin C, which I do not believe
is even known to occur, that is just as much jumping to conclusions as the
reasoning of Pauling.  Note that I am not saying either is correct or 
incorrect; both are consistent with the present data.

The formal use of models in astronomy goes back to antiquity.  In the other
physical sciences, anything non-trivial essentially starts with Kepler and
Galileo, and was advanced in the early stages by Newton and Huygens. 
Quantitative biology is still in its infancy, but it sure is needed for
medicine.  And apart from very simple models, I know of no suggested advance
in the physical sciences which was obtained from data, rather than from
reasoning and confirmed by data, or refuted by data.

Few of the situations in biology or medicine are that simple, and so all 
that can be done from data is to choose between models.  In fact, this is
all that the data did even in the simple cases above; simple models, such
as there existing a linear relationship, or a power law, etc., were already
in the scientist's mind before the data was collected.
-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@snap.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!snap.stat!hrubin(UUCP)
Newsgroups: sci.med