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Statistical Evaluations for
Public Experiment #1
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 #1 (total session)
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 |
| surface: irregular topology |
|
| land: land |
|
| land: natural |
|
| land: level topology |
|
| land: irregular topology |
|
| land: steep peaks |
|
| water: water |
match |
| water: land/water interface |
|
| water: ice or snow |
|
| atmospherics: natural smells |
|
| structure(s) materials: manmade materials |
match |
| structure(s) general location: on/in water |
|
| structure(s) general location: not located on a surface |
match |
| natural object(s): natural object(s) |
|
| natural object(s): on a surface |
|
| subject(s): subject(s) |
match |
| subject(s): male |
match |
| subject(s): female |
match |
| subject(s): one/few |
match |
| mountain: mountain(s) |
|
| mountain: one |
|
| nonsurface structure(s): nonsurface structures |
match |
| nonsurface structure(s): one |
match |
| nonsurface structure(s): subjects inside |
match |
| nonsurface structure(s): subjects nearby outside |
|
| nonsurface structure(s): noticeable relative movement |
match |
| light: bright |
|
| environment: harsh natural |
|
| environment: distant or no base surface |
match |
| energetics: kinetic (fast or slow, one direction) |
match |
| activity: activity or movement by object(s) |
match |
| sounds: talking, shouting, voices |
match |
| temperatures: cold |
|
| dominant session elements: structure(s) not on a surface |
match |
| dominant session elements: natural environment |
|
| sketches: structure(s) not on a surface |
match |
| sketches: natural object on a surface |
|
| sketches: subject(s) |
match |
| sketches: subject(s) in a structure |
match |
| sketches: sloping or peaking base surface(s) |
|
| 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): many/crowd |
| nonsurface structure(s): emitting energetics |
| light: glow |
| energetics: explosive, swirling, or multi-directional movement |
| energetics: fire or heat |
| activity: activity or movement by subject(s) |
| sounds: booming or roaring |
| sounds: wind-type sounds |
| sounds: loud |
| temperatures: hot |
| dominant session elements: movement/activity/energetics |
| sketches: structure(s) |
| sketches: horizontal base surface |
| sketches: radiating or explosive energetics |
The total matches between the session and the target are: 22
The total number of target attributes not perceived: 17
The total number of session entries is: 43
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: 0.564
B. The total matches between the session and the target as a
proportion of the total number of session entries are: 0.512
General session/target correspondence (the average of A and B
above): 0.538
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: 2.797
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: 1.504
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: |
21 |
22 |
| Session 0: |
33 |
17 |
| 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: 24
The total number of session data entries is: 43
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: 0.615
The total matches between the session and the target as a proportion
of the total number of session entries are: 0.558
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: 6.327
The alternative chi-square value with 1 degree of freedom based
on only the distribution of chosen session attributes is: 3.401
TEST
#2: THE RUSSELL PROCEDURE (Click for explanation)
Part I.
The expected mean number of chance matches for this session is:
18.032
The standard deviation (hypergeometric distribution) for this
mean is: 2.385
The 90% confidence interval for this is: [14.108, 21.956]
The 95% confidence interval for this is: [13.357, 22.708]
The 98% confidence interval for this is: [12.486, 23.578]
The unweighted (actual) number of matches between the session
and the target are: 22
The weighted number of matches between the session and the target
are: 46.400
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 22 matches with the target is: 51.916
The standard deviation is: 5.761
Lowest number of pseudo attributes from sample = 34
Highest number of pseudo attributes from sample = 68
The 90% confidence interval for this is: [42.439, 61.393]
The 95% confidence interval for this is: [40.624, 63.208]
The 98% confidence interval for this is: [38.521, 65.311]
Compare these intervals with the actual number of session entries:
43
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 |
0.538 |
1.0 |
| Experiment #3 |
0.348 |
0.392 |
| Experiment #4 |
0.353 |
0.544 |
| Experiment #5 |
0.353 |
0.569 |
| Experiment #6 |
0.347 |
0.520 |
| Experiment #7 |
0.334 |
0.550 |
| Experiment #8 |
0.402 |
0.632 |
| Experiment #9 |
0.693 |
0.219 |
| Experiment #10 |
0.340 |
0.354 |
| Experiment #11 |
0.531 |
0.684 |
| Experiment #12 |
0.847 |
0.495 |
| Experiment #14 |
0.580 |
0.570 |
| Experiment #15 |
0.419 |
0.562 |
The correlation coefficient is: -0.034 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: -0.433 with
an N of 240
The lowest correspondence number for the session and pool is:
0.275
The highest correspondence number for the session and pool is:
0.679
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 remote-viewing session describes its real target's various
characteristics.
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