Statistical Evaluations for
Public Experiment #1
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 #1 (second half)
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 
water: water 
match 
structure(s) materials: manmade materials 
match 
structure(s) general location: on/in water 

structure(s) general location: not located on a surface 
match 
subject(s): subject(s) 
match 
subject(s): one/few 
match 
subject(s): many/crowd 
match 
nonsurface structure(s): nonsurface structures 
match 
nonsurface structure(s): one 
match 
nonsurface structure(s): subjects inside 
match 
nonsurface structure(s): noticeable relative movement 
match 
light: bright 

environment: distant or no base surface 
match 
energetics: explosive, swirling, or multidirectional movement 
match 
energetics: kinetic (fast or slow, one direction) 
match 
activity: activity or movement by object(s) 
match 
temperatures: cold 

dominant session elements: structure(s) not on a surface 
match 
dominant session elements: movement/activity/energetics 
match 
sketches: structure(s) 
match 
sketches: structure(s) not on a surface 
match 
sketches: extensive water 
match 
The target attributes not perceived are:
Missed Target Attributes: 
atmospherics: manmade smells 
atmospherics: smoke or burning (natural or manmade) 
atmospherics: cloud dynamics 
subject(s): male 
subject(s): female 
nonsurface structure(s): emitting energetics 
light: glow 
energetics: fire or heat 
activity: activity or movement by subject(s) 
sounds: talking, shouting, voices 
sounds: booming or roaring 
sounds: windtype sounds 
sounds: loud 
temperatures: hot 
sketches: subject(s) 
sketches: subject(s) in a structure 
sketches: horizontal base surface 
sketches: radiating or explosive energetics 
The total matches between the session and the target are: 21
The total number of target attributes not perceived: 18
The total number of session entries is: 24
The total number of target entries is: 39
A. The total matches between the session and the target as a
proportion of the total number of target attributes are: 0.538
B. The total matches between the session and the target as a
proportion of the total number of session entries are: 0.875
General session/target correspondence (the average of A and B
above): 0.707
The normal chisquare value with 1 degree of freedom testing
the fit of the session to the target based on the table below
is: 27.581
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: 20.463
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: 
3 
21 
Session 0: 
51 
18 
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: 12
The total number of session data entries is: 24
The total number of target attribute entries is: 39
The total matches between the session and the target as a proportion
of the total number of target entries are: 0.308
The total matches between the session and the target as a proportion
of the total number of session entries are: 0.5
The normal chisquare value with 1 degree of freedom testing
the fit of the session to the target based on the table below
is: 0.864
The alternative chisquare value with 1 degree of freedom based
on only the distribution of chosen session attributes is: 0.641
TEST
#2: THE RUSSELL PROCEDURE (Click for explanation)
Part I.
The expected mean number of chance matches for this session is:
10.065
The standard deviation (hypergeometric distribution) for this
mean is: 2.094
The 90% confidence interval for this is: [6.621, 13.508]
The 95% confidence interval for this is: [5.961, 14.168]
The 98% confidence interval for this is: [5.197, 14.932]
The unweighted (actual) number of matches between the session
and the target are: 21
The weighted number of matches between the session and the target
are: 56.137
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 21 matches with the target is: 49.457
The standard deviation is: 5.550
Lowest number of pseudo attributes from sample = 33
Highest number of pseudo attributes from sample = 65
The 90% confidence interval for this is: [40.327, 58.587]
The 95% confidence interval for this is: [38.579, 60.335]
The 98% confidence interval for this is: [36.553, 62.361]
Compare these intervals with the actual number of session entries:
24
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 
0.707 
1.0 
Experiment #3 
0.288 
0.392 
Experiment #4 
0.360 
0.544 
Experiment #5 
0.295 
0.569 
Experiment #6 
0.340 
0.520 
Experiment #7 
0.363 
0.550 
Experiment #8 
0.515 
0.632 
Experiment #9 
0.197 
0.219 
Experiment #10 
0.263 
0.354 
Experiment #11 
0.667 
0.684 
Experiment #12 
0.557 
0.495 
Experiment #14 
0.359 
0.570 
Experiment #15 
0.422 
0.562 
The correlation coefficient is: 0.767 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: 0.900 with an N of 240
The lowest correspondence number for the session and pool is: 0.085
The highest correspondence number for the session and pool is: 0.622
The lowest correspondence number for the target and pool is: 0.037
The highest correspondence number for the target and pool is: 0.855
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.
