Statistical Evaluations for
Public Experiment #12
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: Matthew Pfeiffer
Session: Session #3
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: irregular topology 
match 
land: land 
match 
land: natural 
match 
land: irregular topology 
match 
water: water 
match 
water: land/water interface 
match 
water: ice or snow 
match 
atmospherics: natural smells 
match 
natural object(s): natural object(s) 
match 
natural object(s): on a surface 
match 
subject(s): subject(s) 
match 
subject(s): male 
match 
subject(s): one/few 
match 
mountain: mountain(s) 
match 
mountain: one 
match 
light: bright 
match 
environment: natural 
match 
sounds: noticeably quiet 

temperatures: cold 
match 
dominant session elements: natural environment 
match 
sketches: natural object on a surface 
match 
sketches: subject(s) 
match 
sketches: subject(s) on an outside base surface 
match 
sketches: horizontal base surface 

sketches: sloping or peaking base surface(s) 
match 
sketches: extensive water 

The target attributes not perceived are:
Missed Target Attributes: 
land: steep peaks 
structure(s) materials: manmade materials 
structure(s) general location: not located on a surface 
mountain: multiple 
nonsurface structure(s): nonsurface structures 
nonsurface structure(s): one 
nonsurface structure(s): subjects inside 
nonsurface structure(s): noticeable relative movement 
environment: harsh natural 
energetics: kinetic (fast or slow, one direction) 
activity: activity or movement by object(s) 
sounds: talking, shouting, voices 
sounds: windtype sounds 
dominant session elements: structure(s) not on a surface 
sketches: structure(s) 
sketches: structure(s) not on a surface 
sketches: subject(s) in a structure 
sketches: significant motion of primary object(s) 
The total matches between the session and the target are: 24
The total number of target attributes not perceived: 18
The total number of session entries is: 27
The total number of target entries is: 42
A. The total matches between the session and the target as a
proportion of the total number of target attributes are: 0.571
B. The total matches between the session and the target as a
proportion of the total number of session entries are: 0.889
General session/target correspondence (the average of A and B
above): 0.730
The normal chisquare value with 1 degree of freedom testing
the fit of the session to the target based on the table below
is: 29.374
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: 20.846
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 
24 
Session 0: 
48 
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: 9
The total number of session data entries is: 27
The total number of target attribute entries is: 42
The total matches between the session and the target as a proportion
of the total number of target entries are: 0.214
The total matches between the session and the target as a proportion
of the total number of session entries are: 0.333
The normal chisquare value with 1 degree of freedom testing
the fit of the session to the target based on the table below
is: 2.149
The alternative chisquare value with 1 degree of freedom based
on only the distribution of chosen session attributes is: 1.525
TEST
#2: THE RUSSELL PROCEDURE (Click for explanation)
Part I.
The expected mean number of chance matches for this session is:
12.194
The standard deviation (hypergeometric distribution) for this
mean is: 2.190
The 90% confidence interval for this is: [8.591, 15.796]
The 95% confidence interval for this is: [7.901, 16.486]
The 98% confidence interval for this is: [7.101, 17.286]
The unweighted (actual) number of matches between the session
and the target are: 24
The weighted number of matches between the session and the target
are: 31.432
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 24 matches with the target is: 52.684
The standard deviation is: 4.951
Lowest number of pseudo attributes from sample = 36
Highest number of pseudo attributes from sample = 66
The 90% confidence interval for this is: [44.540, 60.828]
The 95% confidence interval for this is: [42.981, 62.387]
The 98% confidence interval for this is: [41.174, 64.194]
Compare these intervals with the actual number of session entries:
27
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 
0.251 
0.495 
Experiment #3 
0.303 
0.325 
Experiment #4 
0.365 
0.381 
Experiment #5 
0.304 
0.333 
Experiment #6 
0.285 
0.326 
Experiment #7 
0.276 
0.313 
Experiment #8 
0.352 
0.381 
Experiment #9 
0.815 
0.821 
Experiment #10 
0.317 
0.314 
Experiment #11 
0.341 
0.513 
Experiment #12 
0.730 
1.0 
Experiment #14 
0.607 
0.546 
Experiment #15 
0.362 
0.353 
The correlation coefficient is: 0.814 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: 0.813 with
an N of 240
The lowest correspondence number for the session and pool is:
0.162
The highest correspondence number for the session and pool is:
0.826
The lowest correspondence number for the target and pool is:
0.277
The highest correspondence number for the target and pool is:
0.75
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.
