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From: "Steven J. Powell" <sjpowell@access.digex.net>
Date: Sun, 19 Jan 1997 11:49:22 -0500
Fwd Date: Sun, 19 Jan 1997 17:24:17 -0500
Subject: Re: Philosophy of Science and UFOs

>Date: Fri, 10 Jan 1997 11:33:38 -0800
>To: UFO UpDates - Toronto <updates@globalserve.net>, updates@globalserve.net
>From: johnb@island.net (John Bindernagel)
>Subject: Re: UFO UpDate: Re: Philosophy of Science and UFOs

>        I would like to mention two points:

>1. Regarding the issue of whether 60% or 80% or 95% of reports are valid -
>Sasquatch author John Green reminds us that if only one report out of the
>thousands in our database is valid we still have an authentic problem
>(phenomenon?) to explain.

Absolutely.

>2. We scientists like to discard data that does not fit our preconceptions
>or established trends, assumming (hoping?) they are errors in measurement.
>These "outliers" sometimes turn out to be very important and we ignore them
>at our peril. There is the issue of "intellectual integrity"  wherin we
>really should not refer only to the data which support our case and quietly
>omit those embarrassing exceptions and examples which may support an
>opposing view. (We have lots of these in the sasquatch database.)

Terminology is also always an issue.  We don't want to "discard" _any_
data.  Instead, we want find the best subset of data upon which to do some
analysis.  Obviously, we first separate out the data that has a solid or
very good explanation.  We don't discard it or throw it away or use it to
start this evening's fire - we just place it aside in a named subset and
continue on with the remainder.

Here begins the discussion on whether the remainder is 5% ot the total or
30% of the total.  Some favor each extreme (and _both_ estimates are
extremes) and it is really irrelevant which number turns out to be right
(unless it isn't my number <grin>).

With our pre-screened unexplained data we have to make some more decisions.
In this unexplained dataset will still be all sorts of things that
probably need to be separated.  For example, IC1 (Imaginary Case #1) has
one eyewitness to an odd aerial craft.  The eyewitness owns his a very
large farm in Kansas, he's a lawyer and a doctor, a private pilot (retired
USAF B-52 pilot) and pillar of the community.  Sighting duration is under 3
minutes, and the observation is relatively featureless - just a slightly
reflective bright nocturnal object moving around irradically.  IC2 has one
on-ground eyewitness, a teenager, three other independent non-colocated
on-ground eyewitnesses (a diner waitress, a garage mechanic and taxicab
driver) one with videocamera and one with SLR, the local airport tracks
something on radar and local TV crew film several helicopters flying over
the area 30 minutes later.  IC1 has almost no details, IC2 has every
possible detail, over 90% concurrance among the various eyewitnesses to
those details.

Do you put these two sightings in the same dataset - Yes or No - and why?
Rule Number One:  Your reason has to be fully generalized and fully
objective such that the exact same rule can be applied to every other
similar data integrity problem <grin>.

So far I have not mentioned anything about explanatory hypotheses because
it would be premature to do so.  If we can stick to that guideline we can
generally safely avoid the problem of screening our data _based on_
preconceived hypotheses.  If we're to do any screening then it needs to be
objective.

I have a non-answer answer to my question above.  I'd prefer to do a
first-pass pre-screen of the unexplained data and separate out _all_ the
single-witness cases.  Then, with the multi-witness case dataset look for
trends/patterns.  If any trends/patterns are found (whatever they might be)
then look for those in the single-witness case dataset.  If we get matches
_then_ we can start arguing the details.

I imagine that a Bigfoot sighting databse has the same type of problems...