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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.