Approved For Release 2000/08/10 : CIA-RDP96-00789R003800450001-1 UNCLASSIFIED (U) Let [n] be the number of sessions to be analyzed. Suppose also that the descriptor list contains [m] bits. We then define the total number of bits in a specific response, [k], as m R= 2 Ry cmt where R, in 1 if bit [j] were answered affirmatively and equal to 0 otherwise. Likewise the total number of bits in a specific target, [k], as m T= 2 Ty , ae where T, 7 1 if bit [j] were answered affirmatively and equal to 0 otherwise. (U) The accuracy of response, [k], (the percent of target [i] that is described correctly) is given by m Rh i ‘jy The reliability of response [k] (the percent of response [k] that was correct) is given by m RT, ry > as jFl k (U) Finally, the figure of merit for response [k] matched against target [i] is given by (U) The analysis can be considered from two perspectives: matches-——i.e., the figure of merit is calculated by matching a response against its intended target (i.e., k = i), and cross—-matches—-i.e., the figure of merit is calculated by matching a response against some target other than its intended one (i.e., k # i). 5 Approved For Release ood CLASS EEO -soR003800450001-1
Approved For Release 2000/08/10 : CIA-RDP96-00789R003800450001-1 UNCLASSIFIED b. (U) Linear Least-Squares Analysis (U) After [n] remote viewing sessions have been completed and the analysis described above performed using k = i, there are [n] figures of merit, one for each RV session, in order of session number. To examine if there are any systematic variations within this data, a best-fit straight line is fitted through the figures of merit using standard techniques. If [x] is the session number (x=1, ..., n), consider a straight line defined as M(x) = a+b (x-x) where a = M(x) and x = constant, and [a] and [b] are the intercept and slope, respectively. Suppose there are [n] pairs of points, (x,M,). Then the slope, which is calculated by a standard least-squares technique, is given by n n n n 2x My - 2x 2M, b = —L a el, where A is given by A n n 2 A= n » x? - ea x=1 x=1 The intercept is given by , — /, a= a+tbx, where a is given by x M, M> n M, -~ »>x =] x M?> ae Mer ~ * " pany * " _ * " pany If we set x = to the average value of the session number, then x=1 he and [a] becomes the average value of the figure of merit. Thus aes M 6 Approved For Release odd Sb ASSAB1E2}0789R003800450001-1
u ~ Approved For Release 2000/08/10 : CIA-RDP96-00789R003800450001-1 UNCLASSIFIED (U) This analysis produces the average figure of merit for a series and an indicator, the slope, of learning. on (U) Mean Chance Expectation (U) The calculation for the mean chance expectation (MCE) must be sensitive to a number of possible artifacts or confounding factors: e@ Viewer variations (e.g., viewers’ different response biases). General knowledge of the target pool (e.g., targets are known to be National Geographic magazine photographs). @® Specific knowledge of the target pool resulting from trail-by-trial feedback. @ Methodological considerations (e.g., viewers are asked to respond with more data at the end of the series compared to what was asked of them at the beginning of the series). All of these factors will affect the expected average figure of merit and any session-to-session systematic variation that may be present. (U) A method for determining the figure of merit MCE, which requires the fewest number of assumptions about the structure of the data or the response biases, involves the cross-matching of all the responses to the same target set used in the series in question. A cross~match is defined as a comparison between a response and a target other than the one used in the session. If a figure of merit distribution is calculated for a large number of cross—matches, a number of the confounding factors listed above will be addressed. To determine the session-to~session dependencies of the MCE, however, the session order must be preserved. By preserving the order of the responses and by calculating [n] sets of cross—matches at a time, MCE figure of merit, slope, and intercept distributions can be calculated. (U) As before, let [n] be the number of sessions in a series for a single viewer. Also, let the order of the responses, Rx be preserved. Define [N] as the number of cross-match cycles through the ordered set of [n] responses. The MCE calculation proceeds as follows: 1. Randomly choose a target order, i= 1,n , such that k # i where k = 1, n is the preserved response order. 2. Calculate the figure of merit for the kth response/target cross—matches as 7 Approved For Release ObSINGLAS SHHEDo0789R003800450001-1
Approved For Release 2000/08/10 : CIA-RDP96-00789R003800450001-1 UNCLASSIFIED (U) My = 44% Tye k Ai, where a ki and r ii te the accuracy and reliability for response [k] cross-matched against target [i], respectively. 3. Do step 2 above for all [n] sessions. Calculate a slope and intercept for the resulting figures of merit by the linear least-squares analysis described above. 5. Repeat steps 1 through 4 above for [N] cycles to produce MCE figure of merit, slope, and intercept distributions. (U) It is important to note that MCE distributions are generated for each viewer and are not summed across viewers. Therefore, individual viewer differences in response “biases” are accounted for by definition. (U) This procedure also accounts for general knowledge of the target pool by the viewer, because information learned by this method in a given session will not necessarily be associated with the intended target for that session. The net effect of this type of artifact will be to “bias” the MCE figure of merit distribution toward larger values. (U) Because the order of viewings is preserved, any knowledge of the target pool that is learned by the viewer as a result of trial-by-trial feedback is accounted for in two ways: 1. Information resulting from increasing knowledge of the target pool will “bias” the MCE figure of merit distribution toward larger values. 2. Information resulting from increasing knowledge of the target pool as a function of session number will “bias” the MCE slope distribution toward larger values. (U) Similarly, any artifact caused by methodological considerations as a function of session, will also “bias” the MCE figure of merit and slope distributions. d. (U) Probability Assessment and Analysis (U) There are a number hypotheses that can be tested using the various MCE distributions described above: e An individual remote viewing is statistically beyond MCE. 8 Approved For Release UN GASSIELER 00789R003800450001 -1
Approved For Release 2000/08/10 : CIA-RDP96-00789R003800450001-1 UNCLASSIFIED (U) ® The series generated by a single viewer shows statistical evidence for remote viewing. There is evidence above MCE for remote viewing “learning.” The mean of the observed figure of merit distribution is significantly larger than the mean of the MCE distribution. @ The observed figure of merit distribution is significantly different than the MCE distribution. (U) Using the MCE figure of merit distribution, a straightforward calculation of areas will determine whether a particular figure of merit from a single session is significant. Figure 1 shows an example of an MCE figure of merit distribution described above. Mxk is the figure of merit resulting from session [k]. The probability of obtaining a figure of merit of Mxx or larger caused by artifact is the patterned area divided by the total area under the curve shown in Figure 1. This technique can be used to assess the chance likelihood for all sessions in a series by a single viewer. Myx Figure of Merit FIGURE 1 (U) AN MCE FIGURE OF MERIT DISTRIBUTION (U) To determine whether there is statistical evidence of remote viewing within a given series for a single viewer, the p—values for the individual sessions must be combined. The primary method used for combining p-values was developed by Fisher. A X2 with two degrees of freedom is computed for each p-value and summed. The resulting X? is evaluated with 2n degrees of freedom where [n] is the number of p-values that were combined. If [k] is the session number, the appropriate total X? is given by 9 Approved For Release 2000 NAc ASSIRBHEQ)0789R003800450001-1