Statistical Evaluations for Public Experiment
#6
Here are three test procedures that evaluate the remoteviewing 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 Remoteviewing 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 multidirectional 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: windtype 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 chisquare 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 chisquare 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 chisquare value does not take into account the direction of the
relationship between the session data and target attributes. The chisquare
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 
Chisquare 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 CHISQUARE STATISTIC
1. If the value of the chisquare statistic is equal to or greater than
the chisquare 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 chisquare statistic is less than the chisquare
value for a desired significance level, then the remoteviewing 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 chisquare statistic is equal to or greater than
the chisquare 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 consciousmind 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 chisquare 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 chisquare 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 remoteviewing 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 remoteviewing session with a target. The closer a remoteviewing 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 remoteviewing 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 remoteviewing 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 remoteviewing 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 remoteviewing session describes its real target's various characteristics.
