Statistical Evaluations for
Public Experiment #11
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
TEST
#1: Comparing the Remoteviewing session Data with the Target
Attributes (Click for explanation)
The session data are: 
Session/Target Matches: 
atmospherics: manmade smells 
match 
atmospherics: smoke or burning (natural or manmade) 

surface structure(s): surface structure(s) 
match 
surface structure(s): one 
match 
structure(s) materials: manmade materials 
match 
structure(s) general location: on/in water 
match 
structure(s) general location: not located on a surface 
match 
subject(s): subject(s) 
match 
subject(s): male 
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 
light: bright 
match 
energetics: kinetic (fast or slow, one direction) 
match 
activity: activity or movement by subject(s) 
match 
activity: activity or movement by object(s) 
match 
sounds: talking, shouting, voices 
match 
sounds: booming or roaring 
match 
sounds: loud 
match 
temperatures: moderate 
match 
dominant session elements: structure(s) on a surface 
match 
dominant session elements: structure(s) not on a surface 

sketches: structure(s) 
match 
sketches: structure(s) not on a surface 
match 
sketches: subject(s) 
match 
sketches: subject(s) in a structure 
match 
sketches: significant motion of primary object(s) 

The target attributes not perceived are:
Missed Target Attributes: 
surface: surface 
surface: level topology 
water: water 
atmospherics: natural smells 
surface structure(s): multiple 
surface structure(s): subjects inside 
subject(s): focused gathering 
nonsurface structure(s): multiple 
nonsurface structure(s): noticeable relative movement 
environment: natural 
dominant session elements: movement/activity/energetics 
sketches: structure(s) on a surface 
sketches: horizontal base surface 
sketches: extensive water 
The total matches between the session and the target are: 26
The total number of target attributes not perceived: 14
The total number of session entries is: 29
The total number of target entries is: 40
A. The total matches between the session and the target as a
proportion of the total number of target attributes are: 0.65
B. The total matches between the session and the target as a
proportion of the total number of session entries are: 0.897
General session/target correspondence (the average of A and B
above): 0.773
The normal chisquare value with 1 degree of freedom testing
the fit of the session to the target based on the table below
is: 37.405
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: 25.741
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 
26 
Session 0: 
50 
14 
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: 9
The total number of session data entries is: 29
The total number of target attribute entries is: 40
The total matches between the session and the target as a proportion
of the total number of target entries are: 0.225
The total matches between the session and the target as a proportion
of the total number of session entries are: 0.310
The normal chisquare value with 1 degree of freedom testing
the fit of the session to the target based on the table below
is: 2.466
The alternative chisquare value with 1 degree of freedom based
on only the distribution of chosen session attributes is: 1.697
TEST
#2: THE RUSSELL PROCEDURE (Click for explanation)
Part I.
The expected mean number of chance matches for this session is:
12.473
The standard deviation (hypergeometric distribution) for this
mean is: 2.224
The 90% confidence interval for this is: [8.815, 16.131]
The 95% confidence interval for this is: [8.115, 16.832]
The 98% confidence interval for this is: [7.303, 17.643]
The unweighted (actual) number of matches between the session
and the target are: 26
The weighted number of matches between the session and the target
are: 42.239
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 26 matches with the target is: 59.82
The standard deviation is: 5.261
Lowest number of pseudo attributes from sample = 45
Highest number of pseudo attributes from sample = 73
The 90% confidence interval for this is: [51.165, 68.475]
The 95% confidence interval for this is: [49.508, 70.132]
The 98% confidence interval for this is: [47.587, 72.053]
Compare these intervals with the actual number of session entries:
29
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 
0.661 
0.684 
Experiment #3 
0.486 
0.608 
Experiment #4 
0.554 
0.683 
Experiment #5 
0.495 
0.586 
Experiment #6 
0.578 
0.641 
Experiment #7 
0.589 
0.667 
Experiment #8 
0.646 
0.832 
Experiment #9 
0.215 
0.279 
Experiment #10 
0.441 
0.554 
Experiment #11 
0.773 
1.0 
Experiment #12 
0.525 
0.513 
Experiment #14 
0.448 
0.561 
Experiment #15 
0.606 
0.700 
The correlation coefficient is: 0.866 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: 0.925 with
an N of 240
The lowest correspondence number for the session and pool is:
0.038
The highest correspondence number for the session and pool is:
0.787
The lowest correspondence number for the target and pool is:
0.141
The highest correspondence number for the target and pool is:
0.884
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.
