Farsight's Session Analysis Machine (SAM)
TEST ONE: Basic Counts and a Chi-square Test
For Test One, all of the data describing the
perceptions recorded in the remote-viewing session are displayed
together with the matches with respect to the SAM data set for
the target. Those target attributes that are not observed by
the remote viewer are also presented.
Following the description of the session and target data, a variety of
counts are presented. Two important proportions (labeled "A"
and "B") are then presented. Proportion A is the total matches
between the session and the target as a proportion of the total number
of target attributes. If one considers the total number of target attributes
as representing the total variance in the target, then proportion A tells
us how much of this variance is described by the session. When proportion
A is high, then a session has described most of the variance in the target.
Proportion B looks at this from a mirror perspective, and it 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).
Proportion B tells us how efficient the viewer is in describing the target.
Of course, in an extreme and offending case, one can always match all
of a target's attributes by entering every possible attribute available
in SAM when inputting session data. Inaccuracies in this dimension are
revealed by proportion B. When proportion B is low, then a viewer did
not do a good job describing the unique characteristics of the target,
and the best one can say is that accurate target perceptions may be mixed
in with erroneous perceptions. An ideal situation is when both proportions
A and B are high, which means that a target was well described with very
few erroneous perceptions. 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.
Below proportions A and B, a chi-square test
is presented that evaluates the general correlation between the
remote-viewing data and the actual target's attributes. To calculate
the chi-square statistic, a 2X2 table is constructed that associates
a 1 for every session entry or target attribute, and a 0 for
the lack of a session entry or target attribute. An alternate
and more conservative version of the chi-square test which is
based only on the observed session entries is also presented.
The basic interpretation of the chi-square statistic is as follows:
1. If the value of the chi-square statistic
is equal to or greater than the chi-square value for a desired
significance level, and if the correlation between the session
data and the target attributes is positive, then the session's
data are statistically significant descriptors of the target.
2. If the value of the chi-square statistic is less than the
chi-square value for a desired significance level, then the remote-viewing data for the session are not statistically significant.
This normally means that there are decoding errors in the data.
3. If the value of the chi-square statistic is equal to or greater
than the chi-square value for a desired significance level but
the correlation between the session data and target attributes
is negative, then the session either has major decoding errors,
or there may be conscious-mind intervention and/or invention
in the data gathering process.
Following the chi-square analysis, a heuristic
comparison is presented. With this comparison, a pseudo target
is constructed that has the same number of target attributes
as the real target. But with the pseudo target, the attributes
are selected randomly. This heuristic comparison offers a general
idea of how well the remote-viewing data correspond with the
real target as compared with a bogus target. Of course, this
heuristic comparison is an added procedure used for illustration,
not a test.