From: SERTEL@ibm.gwdg.de
Subject: Mars effect: Testing significance.
Organization: GWDG, Goettingen
Date: Sat, 12 Dec 92 11:08:56 MEZ

 
Dear Dr. Nienhuys:
 
this is a reply to your post of November 20 in which you argued
that controls by yearwise shifts of birth data do not allow for
testing the significance of an experimental sample. You said
that randomized samples must be totally independent when
serving as controls against which an experimental sample needs
to be matched.
 
Two comments:
 
(1) I would like to test the significance of those 25.19% French
athletes in Mars sectors (my result with CFEPP data) using an
algorithm that satisfies members of SKEP-groups no less than other
doubters. I therefore ask you whether you would agree with
replacing yearwise shifts by more radical random shifts. That is,
random shifts may be obtained by shifting, among persons, birth
years on the one hand and (birth month + birth day + birth time
as one unit) on the other.  This may be achieved by replacing,
in the list of athletes in alphabetical order, the birth year of
an athlete in position p by the birth year of the athlete in
position p-1, for example.
 
As you may remember, this procedure has already been applied by
skeptics of the the Belgian Comite Para some time ago, using
their own data, but they did not do more than 10 shifts, p-1,
p-2, p-3,...p-10 , as far as I remember.  Apparently it was just
a trial, they did not publish their results and gave up such
work altogether.  Shifts in that fashion, however, could be done
,say, a hundred times or up to p-200, of course, if you like. As
far as I can see, your objections against yearwise shifts as I
proposed them earlier would not apply to such
shifts-within-lists. Am I right?
 
If you would agree I would certainly do such tests, in addition
to my preferred test. No problem would arise if the percentage of
25.19% is neither attained by, say, 100 Nienhuys-preferred shifts
(NPS) nor by 100 Ertel-preferred shifts (EPS), i.e. if the result
is p<.01 in both cases.
 
Let us see what would happen if the results of the two tests
diverge. If EPS would end up with significance and NPS
not (divergence #1), you may not regard the Mars effect as
supported (I would take it as supported, though). On the other
hand, if NPS would end up with significance and EPS not
(divergence #2) you would have to consider the Mars effect as
supported, I would not. My guess is that if divergence occurs,
divergence #2 is more likely to occur than divergence #1. But
that should not bother you if you endorse NPS.
 
My preference for EPS is based on one fair argument: EPS
preserve seasonal and diurnal distributions of births
as well as their interaction. For example, birth times
in June, if different from birth times in December, will
maintain that difference despite yearwise shifts, the difference
will get lost by NPS entailing total randomization.
 
You may object that recent Dutch (and Ertel's) computations have
shown that seasonal variation cannot explain Gauquelin's Mars%
deviations.  However, there are still the Belgians and others
who may keep on suspecting that seasonal and diurnal cycles give
rise to Mars effects.  You know that I myself don't believe that
this is the case, but I would like to satisfy all disbelievers.
 
(2) My second reason for not discarding EPS is that I don't
think your objection against EPS holds. Your computations have
made it clear that G% obtained by yearwise shifts are not
totally random. I found your numbers interesting, but the fact
that you did not find the expected fit among computational
(theoretical) and observed percentages seems to show already
that you probably underrated the impact of what you called,
rightly so, "additional randomization", i.e., variation due to
non-uniformity of Mars and earth rotational speed, retrogade
movements of Mars, ellipticity of Mars orbit, variation of
sector width associated with Mars orbit. We have to keep in mind that
these factors are in fact randomly distributed among athletes
in the empirical sample. By shifting birthdates yearwise these
"additional" factors are again randomized. Inasmuch as these
"additional" factors contribute to G% variation this variation
should  give rise to surprises, 25.19% or more than that
might arise by "additional" randomization and that's all we need.
 
What do we need to know: Is 25.19% within the range of chance
variation or not? Could this level of percentage reoccur,
once in awhile and not too infrequently, by randomizing all
factors involved?  It should be considered as a sign of caution
if the experimenter, when doing these tests, takes pain to
preserve the contribution of possibly hidden terrestrial
artefacts, that is, seasonal and diurnal covariation or socalled
"demographic-astronomical" factors. As a matter of fact,
inasmuch as such hidden factors give rise to or contribute to
an empirical level of 25.19% an advance by such artefacts, if
preserved (EPS preserves it), will keep the probability that
25.19% might reoccur by randomization, at an elevated level. The
decision for EPS is done with care, with fairness, with an
intent to give 25.19% a better chance to reoccur than would be
the case by NPS.
 
Do you agree?
 
If you don't agree with my argument #2, do you agree with proposal
#1 above? No problem for me, in principle, with NPS, if that would
satisfy you. I myself would be satisfied, in addition and even
more so, by EPS.
 
Suitbert Ertel