Farsight's Session Analysis Machine (SAM)
TEST THREE: Correspondence and Correlation
All targets have a variety of descriptive
characteristics (that is, SAM target attributes). When comparing
one target with another, both similarities and differences will
be found between the two. The correspondence numbers are one
measure of the degree of similarity between any two sets of SAM
data, and these numbers can be used to compare one target with
another target, or a remote-viewing session with a target. The
correspondence numbers are calculated as per Test
One. Proportion A is the total matches between the session
and the target as a proportion of the total number of target
attributes. Proportion B is the total matches between the session
and the target as a proportion of the total number of session
entries (not target attributes as with proportion A). The average
of proportions A and B is called the "correspondence number"
for the session, and it is a general measure of the correspondence
between the observed remote-viewing data and the actual target
attributes. Again, correspondence numbers can also be calculated
between any two targets to measure their degree of similarity.
Test three evaluates the correspondence numbers
for each session. The better a remote-viewing session describes
all of a target's characteristics, the higher will be the correspondence
number between the session and the target. Used in this way,
the correspondence number is called the "session/target"
correspondence number. When correspondence numbers are calculated
that compare one target with another, such numbers are called
"target/target" correspondence numbers.
We want to do two things with these correspondence
numbers. First we want to note the relative ranking of the session/target
correspondence number for the remote-viewing session and its
real target as compared with the session/target numbers for the
session and other (bogus) targets in a pool of targets. If a
session describes the actual target relatively well, then its
correspondence number should be high relative to alternative
correspondence numbers for bogus targets selected from a pool.
Second, we want to compare the variation of both the session/target
numbers and the target/target numbers with regard to the pool
of targets. Since a pool of targets normally contains targets
with a great variety of descriptive characteristics, comparing
any given real target with other bogus targets will result in
finding various collections of similarities across the comparisons.
For example, the real target may have a mountain and a structure.
Comparing this target with another target that has only a mountain
will find the similarity in terms of the mountain but not in
terms of the structure. Comparing the same real target with another
target that has only a structure will find the similarity with
respect to the structure, but not with respect to the mountain.
Using a number of comparisons in this way across a pool of targets
allows us to account for all or most of the real target's important
characteristics. This returns us to wanting to compare the variation
between the two sets of session/target and target/target correspondence
numbers across the pool of targets as a means of evaluating the
overall success of a remote-viewing session in capturing its
real target's total set of attributes. When compared with other
targets which in the aggregate contain many different attribute
sets, both the remote-viewing session and its real target should
have correspondence numbers that vary similarly. The correlation
coefficient summarizes this relationship. The correlation coefficient
can vary between -1 and 1. The closer its value is to 1, the
more closely the remote-viewing session describes all of its
real target's various characteristics.
To begin this comparison in test three, correspondence
numbers between the remote-viewing data and all 13 targets that
were chosen for the public demonstration of remote viewing are
calculated and presented in a table. This allows for a direct
comparison of correspondence numbers between the remote-viewing session and the real target as compared with those numbers involving
the other targets in this small pool. An accurate session should
have a correspondence number for the real target that has a relatively
high ranking as compared with the correspondence numbers involving
the other targets. The correlation coefficient for the session/target
and target/target correspondence numbers is also calculated.
A high correlation between the two sets of numbers indicates
that the session data and the target attributes for the real
target for the experiment are similar when compared with target
attributes for other targets in the public demonstration pool.
In Part II of this test, correspondence numbers
for the given remote-viewing session and all targets in a diverse
pool of 240 SAM targets are calculated. Additionally, correspondence
numbers calculated using the real target for the remote viewing
experiment and all targets in the SAM pool are also calculated.
If the remote-viewing session describes the real target well,
then the two sets of correspondence numbers (that is, one comparing
the session with the SAM pool, and the other comparing the real
target with the SAM pool) should vary similarly. Since it is
impractical to examine and compare each pair of correspondence
numbers using this larger pool of targets as is done in Part
I for this test, only the correlation coefficient for the two
sets of correspondence numbers is calculated and presented.