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Statistical Evaluations for Public Experiment #6

Here are three test procedures that evaluate the remote-viewing session with the target data. All of these tests utilize Farsight's Session Analysis Machine (SAM). Click on the test names to get an explanation of each test.

The analyses below are for:
Viewer: Courtney Brown
Session: Session #2

TEST #1: Comparing the Remote-viewing session Data with the Target Attributes (Click for explanation)

The session data are: Session/Target Matches:
surface: surface match
surface: level topology match
land: land match
land: manmade match
land: level topology match
atmospherics: manmade smells match
atmospherics: smoke or burning (natural or manmade) match
surface structure(s): surface structure(s) match
surface structure(s): one match
surface structure(s): multiple match
surface structure(s): subjects inside match
surface structure(s): subjects on base surface outside match
structure(s) materials: manmade materials match
structure(s) general location: on land match
structure(s) general location: on a flat surface match
subject(s): subject(s) match
subject(s): male match
light: bright match
energetics: explosive, swirling, or multi-directional movement match
energetics: kinetic (fast or slow, one direction)
energetics: fire or heat
activity: activity or movement by subject(s)
activity: activity or movement by object(s) match
sounds: booming or roaring match
sounds: wind-type sounds
sounds: loud match
temperatures: hot
dominant session elements: structure(s) on a surface match
sketches: structure(s) match
sketches: structure(s) on a surface match
sketches: subject(s) match
sketches: horizontal base surface match
sketches: radiating or explosive energetics match


The target attributes not perceived are:
Missed Target Attributes:
surface structure(s): city
subject(s): female
subject(s): one/few
subject(s): many/crowd
subject(s): focused gathering
environment: urban
sounds: talking, shouting, voices
temperatures: moderate
sketches: subject(s) in a structure
sketches: subject(s) on an outside base surface


The total matches between the session and the target are: 28
The total number of target attributes not perceived: 10
The total number of session entries is: 33
The total number of target entries is: 38
A. The total matches between the session and the target as a proportion of the total number of target attributes are: 0.737
B. The total matches between the session and the target as a proportion of the total number of session entries are: 0.848
General session/target correspondence (the average of A and B above): 0.7926634768740032
The normal chi-square value with 1 degree of freedom testing the fit of the session to the target based on the table below is: 40.958
The alternative chi-square value with 1 degree of freedom based on only the distribution of chosen session attributes (the top row of the table below) is: 26.425
The correlation between this session's data and the target attributes is: POSITIVE
NOTE: The chi-square value does not take into account the direction of the relationship between the session data and target attributes. The chi-square value is a useful measure ONLY if there is a positive correlation between the target's attributes and the session's SAM entries. (That is, there needs to be a reasonably high number of target and session matches.)

Target 0: Target 1:
Session 1: 5 28
Session 0: 50 10


Chi-square Values: Significance Level:
3.84 0.05
5.02 0.025
6.63 0.010
7.88 0.005
10.8 0.001


INTERPRETATION OF THE CHI-SQUARE STATISTIC
1. If the value of the chi-square statistic is equal to or greater than the chi-square value for a desired significance level in the table above, 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.

HEURISTIC COMPARISON: Comparing the Session with a Target with Randomly Chosen Attributes
The total matches between the session and a target with randomly chosen attributes are: 17
The total number of session data entries is: 33
The total number of target attribute entries is: 38
The total matches between the session and the target as a proportion of the total number of target entries are: 0.447
The total matches between the session and the target as a proportion of the total number of session entries are: 0.515
The normal chi-square value with 1 degree of freedom testing the fit of the session to the target based on the table below is: 2.403
The alternative chi-square value with 1 degree of freedom based on only the distribution of chosen session attributes is: 1.550

TEST #2: THE RUSSELL PROCEDURE (Click for explanation)

Part I.
The expected mean number of chance matches for this session is: 13.484
The standard deviation (hypergeometric distribution) for this mean is: 2.280
The 90% confidence interval for this is: [9.732, 17.235]
The 95% confidence interval for this is: [9.014, 17.954]
The 98% confidence interval for this is: [8.182, 18.786]
The unweighted (actual) number of matches between the session and the target are: 28
The weighted number of matches between the session and the target are: 34.678
INTERPRETATION: If the unweighted and/or weighted number of matches between the session and the target are outside of (that is, greater than) the desired confidence interval, then the number of matches obtained in the session was not by chance.

Part II.
IF THE SESSION DATA WERE RANDOM, HOW MANY SAM ENTRIES WOULD BE NEEDED TO DESCRIBE THE TARGET AS COMPLETELY AS IS DONE BY THE ACTUAL SESSION?
From 1000 Monte Carlo samples:
The mean number of random session pseudo SAM entries that are needed to achieve 28 matches with the target is: 67.271
The standard deviation is: 5.311
Lowest number of pseudo attributes from sample = 48
Highest number of pseudo attributes from sample = 81
The 90% confidence interval for this is: [58.535, 76.007]
The 95% confidence interval for this is: [56.862, 77.680]
The 98% confidence interval for this is: [54.923, 79.619]
Compare these intervals with the actual number of session entries: 33
INTERPRETATION: If the actual number of session SAM entries is outside of (that is, less than) the desired confidence interval, then the number of entries utilized by the remote viewer to obtain the number of matches between the session and the target was not by chance.

TEST #3: CORRESPONDENCE and CORRELATION (Click for explanation)

PART I.
The correspondence data in the table immediately below are computed using the targets from the public demonstration only. The "Session/Target" correspondence numbers are calculated between the remote-viewing session for this experiment and all of the targets used in the public demonstration. The "Target/Target" correspondence numbers are calculated between the real target for this experiment and all of the other targets in the public demonstration pool.

 

Experiment
Number:
Session/Target
Correspondence:
Target/Target
Correspondence:
Experiment #1 0.559 0.520
Experiment #3 0.667 0.906
Experiment #4 0.758 0.802
Experiment #5 0.731 0.827
Experiment #6 0.793 1.0
Experiment #7 0.793 0.938
Experiment #8 0.688 0.720
Experiment #9 0.236 0.285
Experiment #10 0.7 0.865
Experiment #11 0.581 0.641
Experiment #12 0.298 0.326
Experiment #14 0.679 0.734
Experiment #15 0.750 0.917


The correlation coefficient is: 0.877 with an N of 13
INTERPRETATION: All targets have a variety of descriptive characteristics. 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 closer a remote-viewing session is to describing all of a target's characteristics, the higher will be the correspondence number between the session and the target. Since a pool of targets normally contains targets with a great variety of descriptive characteristics, comparing correspondence numbers for the remote-viewing session and its target across a variety of other targets tests how closely the session describes all of the essential characteristics of its real target. When compared with other targets with many different characteristics, 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 its real target's various characteristics.

PART II.
The correlation coefficient is computed as in Part I above, but now using a large (240) pool of SAM targets.

The correlation coefficient is: 0.968 with an N of 240
The lowest correspondence number for the session and pool is: 0.108
The highest correspondence number for the session and pool is: 0.833
The lowest correspondence number for the target and pool is: 0.108
The highest correspondence number for the target and pool is: 0.921
INTERPRETATION: Similarly as with Part I above. The closer the value of the correlation coefficient is to 1, the more closely the remote-viewing session describes its real target's various characteristics.