Posted May 8, 1998
I intend to cover in this paper several topics, related to Witztum et al (WRR) claims asserting that the Equidistant Letter Sequences (ELS) they had found in the Book of Genesis constitute a deliberately inserted "code." WRR base their claim on a statistical study which, as they maintain, had demonstrated that pairs of ELS related to each other by meaning, appear in the Book of Genesis in an unusually "close proximity" to each other. WRR claimed that their statistical data showed extremely low values of "general significance" being in some tests as low as 0.000000017, and in other cases also very small, such as 0.0000028 and the like.
In my other articles in this Web site [1,2,3] I have offered a number of points indicating that WRR's results seemed to contradict basic rules of Probability Theory and Mathematical Statistics; that the basic postulate by WRR, accepting the notion that the deliberately inserted ELS are expected to display "close proximity" has no logical, or factual, or religious foundation; that their results with the Book of Genesis have left many questions unanswered, etc. Critical remarks in regard to WRR's publications, suggested from various viewpoints, and being, in my view, highly convincing, have been voiced, some in the press, but mostly in Web postings, by many other writers, such as Dr. B. McKay, Dr. B. Simon, Dr. G. Kalai, Dr. D. Bar-Natan, A. Gindis, A. Levitan, Dr. J. Price, Dr. A. Hasofer, Dr. J. Rosenstein, Dr. M. Bar-Hillel, Rabbi M. Schiller, D.E. Thomas, G. Cohen, and others (see references in [1,2,3]). In this paper I intend to discuss some topics which so far remained more or less beyond the dispute, but which, in my view, are important additional elements of the case against WRR's claims.
These topics are as follows: 1) Discussion of the foundations of the "proximity" postulate; 2) Discussion of the significance of the variations in the values of four statistics, suggested by WRR and denoted in their publications P1, P2, P3, and P4. 3) Discussion of the data by WRR showing how the criterion of "close proximity" they used changed when the number of permutations of their data lists increased from 1 million to 100 million or to 1 billion.
The original paper by WRR  was published in the Statistical Science journal in 1994. I'll will refer to it as WRR1. Later, WRR offered additional articles, which have not been so far published in scientific press, but have been made available in the form of preprints, Web postings, etc. In particular, one of those articles  was titled "Equidistant Letter Sequences in the Book of Genesis. II Relation to the text." I will refer to it as WRR2. Another article by WRR  which I will refer to as WRR3, was titled "Hidden Codes in Equidistant Letter Sequences in the Book of Genesis. The Statistical Significance of the Phenomenon." That article contains the text of WRR's presentation to the Israeli Academy of Sciences, in 1996. Originally, it was written in Hebrew, but later became available as a preprint in English translation.
In the list of references at the end of this article, there are links to the locations (including postings in this site) where the mentioned three articles by WRR can be viewed.
In WRR1 Witztum et al introduced four quantities, which they named statistics P1, P2, P3, and P4. For each of these four quantities, WRR suggested a formula. (Actually, WRR provided only two formulas, one for both P1 and P3, and the other for both P2 and P4. They applied criteria P3 and P4 to a data list modified as compared to the list utilized when applying P1 and P2. This distinction is if no consequence for our discussion of those four criteria). WRR consider these four quantities as overall statistical measures of "proximity" of ELS pairs in texts under investigation. The lower is the value of any of these four quantities, the "better," according to WRR, is the "proximity" of pairs of ELS under study, in the given text.
We can see that WRR had implicitly suggested several postulates. The first postulate is that there exists such an objective, measurable property of a text, which they named "proximity." The very existence of such a measurable objective property is by no means axiomatic. To verify if such a measure has a real meaning, a rigorous mathematical model of a text must be first developed. No such model was ever suggested. It is possible, that no meaningful single-valued quantity can be defined in a logically uncontroversial manner, which would reflect the behavior of a text in regard to the "proximity" of any text's elements to each other.
There are numerous examples of a situation, when a certain integral property cannot be defined, and therefore any measurements of it are aimless. Some examples of such situations are discussed in the Appendix to this article (items 3 and 4 in the Appendix). Indefinable quantities may be of different nature. Since I am a physicist, it is natural for me to provide an example from Physics. Another reason to consider the following example is the similarity of some of its aspects to the situation with ELS in a text. The example in point is the so called Barkhausen effect. This effect has been thoroughly studied, analyzed, explained, and utilized as a tool for investigation of many properties of ferro- and ferrimagnetic materials. There is a reasonable theoretical model of that effect. Its essence is as follows. If a ferro- or ferrimagnetic sample is being magnetized in an external magnetic field, the magnetization of the sample is increasing, along with the increase of the external magnetizing field. However, even if the magnetizing field is increasing in a continual way, the magnetization of the sample is increasing via thousands of small discontinuities ("Barkhausen jumps").
Many features of this effect has been studied, including the distributions of Barkhausen discontinuities over their duration, and over their amplitude, and over their shape (on magnetization vs time scale). etc. Much is known about the mechanism of those "jumps" and about their relationship to many other properties of the sample, such as demagnetizing factor, saturation magnetization, remanent magnetization, etc. However, there is no such integral quantity which might be named "the value" of Barkhausen effect for a given sample. The difficulty is not mathematical. The physical nature of the effect is such that no single-valued integral property of a sample can be defined which would characterize Barkhausen effect in a logically consistent way.
In a certain sense, the phenomenon of ELS that are present in a text in large numbers, and are somehow distributed over their lengths, and over their skips, etc, has certain features in common with Barkhausen "jumps." Of course, similar does not mean identical. The nature of ELS is quite different from Barkhausen's jumps, but there is enough of a similarity to guess that possibly there is also no such an integral single-valued characteristic of a text as the "proximity" between ELS.
There is a reasonable mathematical description of a ferromagnetic body and of Barkhausen effect. Even having such a description is not sufficient to define a quantity to be named "Value of Barkhausen effect." There is no such mathematical descriptions of a text. Without it, there is no certainty that it is possible to define, in non-controversial terms, a "proximity" between any elements of a text, as a single-valued quantity.
Some other examples (one of which, namely example 3 in the Appendix, is closer to WRR's actual calculations) illustrating a situation where an integral criterion of a certain property of a conglomerate of elements cannot be defined, are given in the Appendix to this article (example 3 and 4 in the Appendix).
Hence, if a quantity named "proximity" is suggested, its very objective existence is not axiomatic, but has to be postulated. If it happens that such a quantity is not definable, then different methods developed for its measurement will most likely produce different, and meaningless results. The verification of the postulate in question can be performed by considering the results of that quantity's measurement and judging, by its behavior, if indeed the measured quantity behaves in a non-contradictory way. (We will try to judge the behavior of the "proximity" suggested by WRR to see if it behaves in a reasonable way).
Now, let us follow WRR, and accept the postulate about the existence of a measurable quantity they named "proximity." The next postulate inevitably to be formulated, either explicitly, or implicitly, will relate to the quantitative measure to be chosen for "proximity's" calculation. One thing is to accept the postulate that "proximity" is an objectively existing property of a text, but another thing is to define its measurable characteristics. WRR suggested four aggregate measures of "proximity," P1, P2 , P3, and P4.
The question which arises is, why four different measures?
There can be various reasons for adopting more than one experimental criterion of a phenomenon. One such reason is often the desire to verify the measurement's results by two (or more) independent methods. We will see, though, that this was not the reason behind WRR's choice of four "statistics." Indeed, in many tests, WRR calculated only 2, and in a number of tests, only one out of four P's. Their justification for the choice of this or that of four P's was, as they indicated, the choice of that P which generated the "best" results. As it can be seen from WRR's articles, they realized that each of four P's has certain limitations, and therefore they considered it necessary to derive four measures, each allegedly for a specific purpose. In the actual use of P's, however, WRR simply used a P that produced a "better" outcome of their measurements.
Whatever reason may WRR have for the derivation of four separate measures of "proximity," if these measures have any objective contents, they necessarily must produce results that do not contradict each other.
As we will see, the results obtained by using various P, almost without exception, actually did contradict each other.
Let us look at the following situation. Assume we want to measure a certain property X of two objects of the same nature, object A and object B. We are offered two different methods to measure X. When the first method is used, the values of X turn out to be XA1 for A and XB1 for B. When the second method is used, we find instead values of X to be XA2 and XB2 . Analyzing the results of the measurements, we see that the first method showed that XA1>XB1, but the second method showed that XA2<XB2. So, using one method, we found that property X has a larger value for object A than it has for B, while using the second method, we found that property X has a larger value for object B than it has for A. These results are mutually exclusive. The unavoidable conclusion is that either method 1, or method 2, or perhaps both methods are unreliable. One more possible explanation may be that X is simply not an objectively existing property of our target objects A and B.
After the first version of this paper was posted, Mr. Alec Gindis (private communication) suggested that I add a more specific example of the above described situation. Such more specific example is given in the Appendix to this article (example 1 in the Appendix).
The above described unfortunate situation is what happened in WRR's tests, as it is shown in the following two subsections.
For all of the tests described in WRR1, and for many tests described in WRR3, WRR had calculated values of P for the explored texts, using both the "correct" list of "appellations/dates" (see the explanation in ) and a large number of its scrambled versions which served as controls. In some of the tests described in WRR3 (like those tests referred to as "Title sample sets") WRR used a different method. In those cases, only one "title" expression served for all permutations. In these tests, the process, briefly, involved permutations of the letters in one of the words of the "words pairs" under investigation, while preserving the other word in the pair, namely the so called "title," intact. While referring to both permutations methods used by WRR, we will be using words "data lists" or simply "lists" for both techniques, where in one situation such "data lists" contained only one "title" expression vs a multitude of "matching" expressions, while in the other situation the "data list" contained two groups of "matched" expressions, which could be mismatched by shuffling one group vs the other).
Naturally, as formula for P1 and P3 differs from that for P2 and P4, and, also, the structures of data lists for P1 and P2 are slightly different from those for P3 and P4, values of P's are also different for each of P1, P2, P3, and P4. That is what is expected, of course. Next, though, WRR place the obtained values of each of four P's in the ascending orders. They assign to the version of the data list that turned out to have the minimal value of P, rank 1. The version of the permuted data list that has the next smallest value of P is assigned rank 2, etc. Somewhere on the ladder of ranks so created, there is the original, not scrambled data list. Let's say it has rank r. It means that in the entire set of tested data lists, there are r-1 scrambled lists, whose rank is lower than for the "correct," not scrambled list.
What happened in WRR's tests, was that ranks r found for the "correct" (non-permuted) lists were different for each of four versions of criterion P. Let us see what is the meaning of that situation. If on the ladder of ranks corresponding to P1, the rank of the "correct" list is r1, there are in that ladder r1-1 scrambled lists with ranks below that for the "correct" data list. On the other hand, if on the ladder corresponding to criterion P2, the rank of the "correct"list is r2, then, according to P2, there are not r1-1 but r2-1 scrambled lists with a criterion of proximity P below that for the "correct" list. Then, if, for example, it has been found that r1>r2, then obviously there are at least (r1-1) - (r2-1)=r1-r2 lists which, according to criterion P1 have "proximity" below that for the "correct" list, while, according to criterion P2, the same, at least r1-r2 lists, have "proximity" higher than that for the "correct" list. The same consideration applies to the different "ranks" found for P3 and P4.
Consider a numerical example. In Table 8 in WRR3, the value of the "rank" for P1 is given as 14, while the value of the "rank" for P2 (for the same sample set, and in the same test) is given as 2723. Hence, if we believe the value of P1, there are in that sample set only 13 permuted lists for which the "proximity" is "better" than for the non-permuted list. However, if we believe instead the value of P2, then there are, among the explored permutations of the list, not 13, but 2722 versions with the "better" proximity than for the non-permuted list. In other words, there are, among the explored permutations of the list, at least 2722-13=2709 lists which, according to the values of P1, have a higher value of "proximity" measure than the original, non-permuted list, but according to P2, the same, at least 2709, lists have a lower value of the "proximity " measure than the original, non-permuted list. The results, obtained by two WRR's methods for those, at least 2709, permutations, are mutually exclusive.
The inevitable conclusion is that either P1, or P2, or both of them are not objective criteria of the "proximity," or, possibly, that the "proximity" itself does not exist as an objective property of the text. It applies also to P3 and P4.
Except for one test only, namely that for the "Nations sample set" (table 5 in WRR3), in all other tests where WRR reported values for more than one of four P's, the values of "ranks" reported by WRR turned out to be different for each P.
Therefore, if "proximity" itself, as implicitly postulated by WRR, is indeed an objective property of the texts, then the only possible conclusion is that at least two of the four P (for example, P1 and P3) or perhaps, all four of them, are not objective measures of that "proximity."
I believe that the above simple consideration alone renders invalid the results and conclusions offered by WRR (see more about it in the Appendix, examples 1 through 4).
Admittedly, the above considerations may meet rejection on the part of people specializing in Mathematical Statistics, since their mindset may well be quite different from that of a physicist. Indeed, in Mathematical Statistics there is an established procedure for "hypothesis testing." As one can though find in any text of Mahematical Statistics, the concept of a general scientific hypothesis is not the same as the concept of a hypothesis to be tested in Mathemathical Statistics. For example, the following scientific hypotheses cannot be subjected to the statistical hypothesis test: a) Hypothesis that the diameter of Mars is smaller than that of Venus. b) Hypothesis that all energy on the earth has its origin in the Sun, c) Hypothesis that in a specific car accident the guilty party was the truck driver who sped across the intersection, etc. Actually, there is more scientific hypotheses that cannot be subjected legitimately to a statistical hypothesis testing than those that may.
A statistical hypothesis necessarily deals with random variables. Otherwise a hypothesis may not be treated statistically.
This difference between a statistical and a general scientific hypotheses is conducive to the development of different mindsets among, say, physicists, on the one hand, and specialists in Math. Statistics, on the other. As one of the consequences, while physicists may sometimes underestimate or misinterpret the validity of statistical data, the specialists in Math. Statistics are naturally inclined to sometimes attribute to a statistical test more cognitive value than it is warranted by the power of that test. The results of a statistical test, while often possessing a very strong cognitive significance, in many other cases may lack it either partially or completely.
Consider the following trivial example. Assume a study has been conducted which has proved statistically that there is much fewer cases of tuberculosis among people owing a golden wristwatch than among people owing no watch or a watch made of steel. Even if that study has been conducted impeccably from the standpoint of statistics, showing a very strong correlation between the ownership of gold watches and rare occurrences of tuberculosis, obviously it would not at all mean that a gold watch is a good cure for tuberculosis.
Switching to the language of Math. Statistics, we may assert that rejecting a null hypothesis in favor of the alternative hypothesis, which is the legitimate outcome of a statistical test, never means that the alternative hypothesis is correct. It only means that, within the framework of the particularly formulated problem, the alternative hypothesis is more likely than the null hypothesis. Sometimes such a conclusion may have a very solid cognitive value. Often it may not.
Returning to the case of four statistics P1 , P2 , P3 , and P4 in WRR, I would like to indicate that, even if the data set chosen by WRR were "correct" (and even that is highly doubtful) then the low ranks of the identity permutations, different as they are for different P, can be considered as a sufficient ground to reject WRR's null hypothesis. As to an altertnative hypothesis, WRR have never formulated it. It seems though to boil down to their final statement that "close proximity of related ELS in the Book of Genesis is not due to chance." And that is precisely where the discrepancies between the ranks of the identity permutation, obtained through different P, indicate that the rejection of their null hypothesis by no means signified the acceptance of their not-formulated alternative hypothesis, other than within the very narrow limits of a purely statistical evidence, which is though quite insufficient as a general scientific evidence. Low ranks of the "correct" list of appellations/dates, which are different for different criteria P1, P2 , P3 and P4 have no more of a proof value than has the correlation between gold watches ownership and tuberculosis.
The considerations of subsection c) related to the behavior of the alleged aggregate criteria of "proximity" P1 , P2 , P3 , and P4 , when all four of them were applied to the same sample set and in the same test, which of course was of the paramount significance for the determination of the validity of those criteria. Now let us see how these alleged measures of "proximity" behave when exploring different sample sets.
Table 1 in WRR3 provides the "ranks" of the original, non-permuted data list among 1 million "competitors," for the case of the "2nd Sample set." Let us denote the rank determined by using Pias r i. The ranks in Table 1 are in the following ascending order: r4 < r2< r1 < r3. (The lowest rank of the non-permuted list among 1 million competing permutations, namely 4, was obtained using criterion P4; the next lowest rank, namely 5, was obtained by using P2 , the next one in the ascending order of ranks, namely 453, was obtained by using P1 , and the highest rank out of four ranks, namely 570, was obtained using criterion P3).
Now look at table 3 in WRR3, where the results of a test on the "1st sample set" are given. Now the ascending order of ranks of the non-permuted list among 1 million of "competitors" is as follows: r2 < r4 < r3 < r1 . It is a completely different order of r's as compared with that in table 1. For example, while in the test on the 2nd sample set (table 1) criterion P4 produced the lowest rank out of four measured ranks for the non-permuted list, and criterion P3 produced the highest rank for the same non-permuted list, in the test on the 1st sample set (table 3) the lowest rank was produced by P2 , and the highest rank, by P1 , etc.
Look now at table 8 in WRR3. It relates to the test conducted on "Title sample set B". In that table are given only the ranks of the non-permuted list (among 100 million competitors) obtained by using only two of the four criteria, namely P1 and P2. It was found in that test that r1 < r2. This order of "ranks" is again different, both from those given in Table 1 and Table 3.
Hence not only different P's produce incompatible values of "ranks" in the same test, as it was shown in subsection c) above, there is also no consistency whatsoever in the orders of ranks these P's produce in different tests. Any Pican produce a lower rank of the non-permuted list than any other Pj in some test, but in another test the same Pican produce a higher rank than Pj .
Such erratic behavior reinforces the conclusion of subsection c) and leads to the suggestion that indeed the values not only of any two of P's but rather of all four of P's are accidental numbers without any objective contents.
(The behavior of P's described in the above two subsections hints at a possible deeper fault of the WRR's procedure than just the unreliability of four P's themselves. Namely, the "metric" ("c-value,") which was the starting point for the calculation of all four "statistics," was apparently defined by WRR in an unnatural way, not reflecting a real meaningful "distance" between ELS. Indeed, as A. Hasofer  has shown, it is easy to construct examples where c-value will produce "large" distances for ELS that are obviously close to each other, and "short" distances for the ELS that are obviously located remotely from each other).
Hence, just by viewing the behavior of the "proximity" measures they suggested, WRR had to derive the only one possible conclusion, namely that there was something wrong either with their experiments or with their interpretation of the observed data. Unfortunately, WRR chose to except the obviously doubtful results as scientifically sound. That is not how a scientific research is supposed to be conducted.
In WRR3 , which, as mentioned before, is the thesis of WRR's presentation to the Israeli Academy of Sciences in 1966, these authors reported on additional experiments performed after WRR1 was published. Most of the material in WRR3 repeats WRR1. There are a few new elements, though, in WRR3 as compared with WRR1. One such element is the addition of results of experiments conducted by H. Gans, who used the same technique as in WRR1 but this time exploring a possible "code" connecting the Rabbis names not with their dates of birth/deaths, but with locations they were born or died in. Another additional set of tests was conducted by WRR in which the names of 68 nations derived from the names of Noah's descendents, were matched to four "characteristics" of these nations (for example, its language). These additions did not add anything principally new to the previous results reported by WRR, as they were based on exactly the same technique and applied to the same basic text.
Another new element in WRR3 as compared with WRR1 was the extension of their measurements from one million permutations to one hundred million of permutations, and, at least in one case, to one billion of permutations. Surveying the material gathered in WRR3 shows some remarkable features in their tables of experimental data.
Before discussing the particular data in WRR3, let us make some very simple calculations. As mentioned before, WRR provide in their tables the values of what they call "ranks" of the "correct data" lists, where data lists in some tests were appellations of famous rabbis' vs their dates of births or deaths, while in some other tests, reported in WRR3, the data list comprised, for example, names of the Rabbi's vs names of the locations where the Rabbis were born or died, and the like. If the "correct" list had rank r, it means there were found g=r-1 scrambled lists whose "proximity" criterion P was found to be smaller than for the "correct" list.
"Rank" is not an extensive quantity, as it is simply the serial number of a certain permutation on an arbitrarily chosen scale, where the values of P are placed in an ascending order, and for each P in the ladder so created, a real natural number is assigned in the ascending order of natural numbers. Since "rank" is not an extensive quantity, its mean value has no material meaning.
Let us denote the total number of explored permuted lists as N. For example, in WRR's articles, N was in some cases 1 million, in some other cases 100 million, and, at least in one case, 1 billion. Let us consider the overall set of N permutations as the sum of n subsets, each subset comprising m permutations. For example, if N is 100 million, then we consider it being the sum of 100 subsets (n=100) each comprising 1 million (m=1,000,000) permutations. If in any particular subset of permutations, whose serial number is i, it was found that the rank of the non-permuted text wasri, then obviously gi=ri-1. Unlike rank r, quantity g is an extensive one, and the sum of its values has a very simple meaning. If n subsets of tests have been performed, and in each of them the value of giwas found, then for the entire set of N permutations, the value of g (we denote it g*) can be found simply by summation of all gi:
Since ri =gi+1, obviously the rank r* of the non-permuted text in the entire set of N permutations, which set consists of n subsets, is
Using this simple formula, we can easily find what is the rank of the non-permuted list in the entire set of N permutations, if we know values of ranks of that list in each of n subsets of permutations.
WRR never provided the information derived from all n subsets of permutations. In WRR3 there are though two cases when the information is available for both the set of N permutations, and one subset of m permutations.
Let us look at these results.
Table 1 in WRR3 provides the values of all four P's for the "2nd sample set." The minimum value of rank of the non-permuted list among the four P happened to be for P4 and equaled 4. This rank of the non-permuted list of appellations/dates was obtained in the subset of 1 million permutations. Table 2 provides the results of an analogous test, but this time with 100 million permutations. True to their practice of choosing the "best" outcome, WRR made measurements, in the larger sampling, for only one of the P's, namely for P4 , and the corresponding rank was found to be 59.
Since the entire set of 100 million permutations consists of n=100 subsets , in each of 1 million permutations-long subsets the mean value of g over one subset is g m = g*/100= (r*-1)/100= =(59-1)/100=0.58.
If the mean value of g per one subset of 1 million permutations is 0.58, what is the probability that in an arbitrary subset the value of g will happen to be 3 (which corresponds to the rank of 4 reported in table 1)?
We can estimate the probability in question by assuming that in the 32! combinations of all possible permutations of the data lists, the values of g are distributed following Poisson distribution . Then, as it was actually calculated by Dr. B. McKay (private communication) the probability of g in an arbitrarily chosen subset to be 3 is between 0.02 and 0.03. Of course, even though this probability is rather small, it is not exceedingly so. However, this is just the beginning of the story.
Looking at the rest of the tables in WRR3, we notice, that besides the above described tables 1 and 2, there is only one more case for which WRR provided data both for the entire set of N permutations, and for a subset of it. These are tables 5 and 6 containing the data for the "Nations sample set." In table 5 the rank of the non-permuted list among 1 million permutations was found to be 1, both for P1 and P2. This is a result which is rather exceptional among all the results obtained by WRR, as well as by H. Gans, by B. McKay, etc. Rank of 1 was almost never observed in all the multitude of experiments described so far. In table 6, for the same sample set, but for 1 billion of permutations, the rank, reported only for P2 , is 17, which is also the "best" of all the results reported so far. Now, of course, if the rank among 1 billion permutations is 17, then the occurrence of rank of 1 in a subset of one million permutations is not contradictory. The question is, however, how reliable are these exceptionally low ranks for the "Nations" sample set, presented in tables 5 and 6 in WRR?
A convincing answer to that question provides an article by D. Bar-Natan, B. McKay, and S. Sternberg . These three authors have thoroughly analyzed the "Nations" experiment" by WRR and have demonstrated that the results in question are highly unreliable. This leaves only one way for us, which is to dismiss the astoundingly "good" results of the "Nations" experiment.
Then let us look at the rest of the data presented in WRR3. For some sample sets WRR used 1 million permutations only. For others, 100 million permutations only. They did not provide any explanations as to why they chose different numbers of permutations for different sample sets. A natural assumption in this situation is, especially given the inclination of WRR to choose for presentation the "best" P, that their decision to choose this or that number of permutations was also somehow influenced by the desire to present the best results only.
Let us look, for example, again at table 8 in WRR3. It contains the data for 100 million permutations for what they referred to as "Title type, B sample set." As mentioned earlier, in that table values of ranks are 14 for P1, and 2723 for P2. Of course, when calculating the "significance level" WRR ignored the larger number, and used the rank of 14. If in one hundred million permutations the rank was 14, it means the mean value of g was gm =(r*-1)/100=0.13. In order for the mean g to be 0.13 in the totality of 100 subsets, its value in most of those 100 subsets must have been 0, with only a few subsets with g=1 or larger. In other words, the value of the rank in most of the 1 million-long subsets must be 1, and only in a few subsets it may be 2 or more. Except for the "Nations" sample set, which has been proved unreliable, WRR have never reported such low ranks for any sample sets.
Similar situation is with, for example, table 7, which provides data for "Title type, A" sample set. The rank of the non-permuted list in 100 million permutations was reported to be 24. It means the mean value of g in each of the one million-long subsets must have been 0.23. Again, it requires that the ranks in most of the one-million-long subsets have values of 1, and only in a very few of them more than 1. It again is not a common situation as it was observed in WRR's other experiments.
Similarly "good" are the data in table 10, where the "Title type, D" sample set is reported. In that case, the minimum value of rank for P is only 11 out of one hundred million permutations. It means the mean value of g=0.1, and the formal "mean" of the rank r to be 1.1. To get such result for 100 million permutations, one needs to find the rank to be 1 in the overwhelming majority of the one million permutations-long subsets. Except for the discredited "Nations" sample set, WRR never observed such values of ranks when exploring one million permutations-long sets.
Therefore the results reported in WRR3 look strange. Each time WRR increased the number of permutations from 1 million to 100 million, or to one billion, their results improved. One million, one hundred million, or even one billion all are just tiny fractions of the total number of possible permutations of the data list which was 32!. The difference between one milion and one billion is 3 orders of magnitude. On the other hand, even one billion is smaller than 32! by more than 26 orders of magnitude. Therefore switching from one million of permutations to one hundred million, or even to one billion of permutations hardly changed the fact that the used permutations still constituted a randomly selected very small subset of the total set of possible permutations. Hence, we could expect that the "rank" of the non-permuted list would vary between subsets of one million and of one hundred million, or even of one billion permutations, in a random fashion, rather than to display a tendency to a measurable improvement of results with the increase in the number of permutations explored.
If, say, in each case the probability of the reported numbers to happen by chance was the same 0.02 to 0.03, as in the case of tables 5 and 6, then the probability of all of those results to happen as a combination, by chance, equals the product of all those 0.03's. For example, if the results in the four tables shown in WRR3 are taken into account, the probability of all shown results, in their combination, to happen can be roughly estimated as about 0.00000008. (Of course, this number has no real significance, but this estimate shows how very small values of "probabilities" could be arrived at. Likewise, the very small "significance levels" produced by WRR are not really of a substantial cognitive value).
Hence, there was a strange systematic improvement of ranks reported by WRR when they increased the number of permutations, the probability of this systematic increase being very small.
The explanation which comes to mind is, that, in agreement with the considerations of the previous section of this article, quantities P1 , P2 , P3 , and P4, suggested by WRR as alleged measures of "proximity" of conceptually related ELS in the book of Genesis, actually are not objective measures of some objectively existing quantity.
If the aggregate "statistics" used by WRR under the names of P's are not reflecting any objective property of texts, what criteria can be suggested instead? To answer this question, let us go back to the example given at the beginning of this article, namely the example of Barkhausen effect. As mentioned earlier, Barkhausen effect has been thoroughly studied, understood, and utilized to unearth many subtle features of the behavior of ferro- or ferrimagnetic samples. One common feature Barkhausen effect has with ELS in texts, is that both are conglomerates of many elements. In the case of Barkhausen effect these elements are Barkhausen discontinuities (magnetization "jumps") while in the case of texts these elements are pairs of conceptually related ELS.
However, the study of Barkhausen effect proceeded on a path very different from that chosen by WRR and by some other people for studying ELS. To investigate ELS pairs, WRR as well as some other people, who followed WRR in that approach, chose to utilize cumulative measures, exemplified by "statistics" P1, P2, P3, and P4. On the other hand, the scientists who investigated Barkhausen effect, concentrated mainly on studying distributions of Barkhausen "jumps" over their duration, over their amplitude etc. Of course, Barkhausen effect is just one example of such an approach. Physicists usually are aware of the limited value of the integral, cumulative measures, and, whenever possible, try to unearth the distributions of any effect's elements over its characteristics. The distributions are much more informative. Real statistics is in the distributions.
(For those mathematically inclined, here is a simple example from calculus. If an integrand expression is known, as well as the limits of integration, the value of the integral is defined in an unambiguous way. On the other hand, if a value of an integral is known, it does not reveal by itself what kind of an integrand expression is responsible for that value of the integral. The same value of an integral can be due to many different functions as integrands. In particular, if a distribution function is known, there is normally only one, quite definite value of its integral at given integration limits.The opposite statement is not true).
Choosing cumulative measures, be it P1, P2 etc, or any other similar quantities, means sacrificing the scope of information about the object, in this case a text, for the sake of simplification. Therefore, even if P1, P2 etc were replaced with some other, better chosen cumulative quantities, there is little hope it would provide a reasonable proof of either the presence or of the absence of a "code" in a text.
I am not a computer programmer, but it certainly could be possible to develop a program capable of analyzing the distributions of ELS pairs over the ELS' lengths, skips, "distances" between them, etc, plus a concomitant analysis of their spacewise distribution in the text ("mapping" the text in regard to ELS locations).
Since skip's and word's lengths are unambiguous concepts, no problem should arise with interpreting the distributions of ELS over the words' and skips' lengths. On the other hand, distributions over "distance" between conceptually related ELS would be more problematic because of the uncertainty in the "distance" definition.
One possible way to circumvent that problem could be to account for the fact that much of the uncertainty in the "distance" between ELS is contributed by the variations in the skips' lengths and words' lengths. For a subset of ELS all having the same skip and the same word's length, the definition of the "distance" would become much easier to choose. Then, rather than studying one, overall distribution which would encompass ELS' with all possible words' and skips' lengths, several separate distributions over the "distance" between the conceptually related ELS should be studied. Each such separate distribution would be determined for a "bin" containing ELS with only a specified value of skip and a specified word length.
An example of a situation where a cumulative measure provides for a meaningless and misleading conclusion while a study of distributions sheds light on the actual phenomenon, is given in the Appendix (example 5 in the Appendix).
Possibly, the described combination of distributions, including the "map" of ELS, would reveal certain patterns, specific for various texts. If it were the case, an argument in favor of a "code" could be then an indisputable uniqueness of the pattern in question in the Bible, as compared to all other texts. In other words, such a hypothetical unique pattern must disappear if the Bible text is randomized (for example, permuted in whichever way) and also no such pattern must be found in any real texts other than that of the Bible (possibly only of some specific part of the Bible). Of course, to perform such a study would be quite time consuming and tedious. While I would not want to make predictions, my own feeling is that the result most likely would be still inconclusive, since uniqueness of a specific text in regard to distribution of various characteristics may occur naturally by many mechanisms, and not necessarily proves a deliberate design. However, without such a study, WRR's claims about the existence of a "code" in the Torah, on the base of calculating some meaningless cumulative quantities, are even more contrary to the accepted scientific procedure.
Comment. Since the initial version of this article had been posted, a new information has become available, proving again, that when its time comes, a similar idea occurs simultaneously and independently to a number of people. (All the information I am referring to in this comment has been obtained via personnal communications).
a) Dr. R. Haralick, apparently being dissatisfied with the prospects of solving the controversy about the "code" by means of continuing experiments employing criteria similar to WRR's four "statistics," suggested that some other characteristics of a text have to be explored. Such characteristics would be identified in the text of the Bible and then tested to see if they disappear when the text is randomized. (Dr. R. Haralick usually refers to randomized texts as "monkey" texts). Analogous tests would be performed with non-Biblical texts. This would enable the researchers to determine if certain characteristics are unique for the Bible text. Dr. R. Haralick suggested two possible candidates for the characteristics to be explored, namely 1) Word frequency, and 2)Word clumping. It is easy to see that Dr. Haralick's idea jibes well with my suggestion in regard to studying the distributions of ELS over their parameters, the difference being in the choice of texts' characteristics to be studied (Dr. R. Haralick invited everybody to suggest other possible characteristics to investigate; he apparently had no knowledge yet about my proposal about ELS distributions). Of course, many problems remain if Dr. Haralick's proposal is accepted for a real experiment. These problem relate both to a proper choice of the suitable text's characteristics and to the interpretation of the results. Moreover, the chosen characteristic must not only be suitable in principle, it must also be relatively easy to measure.
The ELS distributions over their parameters, suggested above, do not have an inherent evidentiary advantage as compared with any other possiblle characteristics of texts. Since, however, until now, the discussion, for obvious reasons, revolved around ELS, it gives the ELS distributions, within the framework of the ongoing dispute, a certain special place among all properties of texts. Studying the ELS distributions seems to be the easiest way to connect the outcomes of such experiments to WRR's results, which may be not the case if some other characteristics of texts are chosen for exploration. (Also,"mapping" the text in regard to ELS spatial distribution, as suggested above, seems to be a wider concept than just determining word "clumping," as the "map" would include any evidence of "clumping" as a part of the overall picture of words' spatial distribution).
b) Dr. B. McKay went further, having not just suggested to explore various peculiarities of meaningful text in comparison with their randomized versions, but has actually performed an extensive series of ingenious experiments in this direction. Among the features Dr. B. McKay studied are the following:
1. Correlation between various letters situated in a close proximity to each other. (For example, in English letter q is very often followed by u, etc). 2. Non-even distribution of letters across the entire text. 3. Non-even distribution of letters within the sentences; 4. A correlation between letters occupying certain positions in one word and letters occupying the same position, or different, but fixed, position, in another, closely situated word (for example between the first letter in one word and the first letter in another word, or between the first letter in one word, and the last letter in another word, etc). 5. Variations in letters frequencies between left and right halves of verses in the Bible (and a similar phenomenon in non-Biblical texts) as compared with randomized "texts" etc.
In all the above situations Dr. McKay found strong effects in the meaningful texts, which disappeared in randomized texts. The phenomena were similar in both the Books of the Bible and in non-Biblical texts. (Since all the above features of meaningful texts contribute to the entropies of texts, these finds are in a good agreement with the hypothesis about the possible role of texts' entropy in making WRR's "proximity" values nearly extreme in the actual Genesis text as compared with control texts - see ).
As Dr. McKay indicated, while all the effects he discovered must be connected in a certain way to the ELS behavior, the exact manner of such connections is hard to figure out. In view of this, the study of the distributions of ELS over their parameters, while not being inherently a stronger evidence either for or against the "code" than any other features of texts, would have an advantage of being more directly reflecting on the ELS behavior, which has, so far, been at the core of the "code" controversy.
The above considerations lead to the following conclusions:
1. The postulate implicitly introduced by WRR in regard to the objective existence of a property of texts they named "proximity" found no confirmation in the results of the experiments reported by WRR.
3. The above two conclusions are in agreement with the other arguments against the claims by WRR, offered in the other articles in this Web site.
4. A better way to study the phenomenon of conceptually related ELS would be the investigation of their distributions over various parameters rather than the use of cumulative measures.
Whereas there is no proof availavle that there are no "codes" in the Bible, the alleged proofs suggested so far in favor of the hypothesis of the "code's" existence, do not meet a number of necessary requirements to be accepted as real. Until (and if) such rigorous proofs are offered, the most reasonable explanation of the data reported by WRR remains the suggestion that the phenomenon is due to random coincidences of ELS.
Example 1. The case of two faulty measuring devices
As it was indicated in the body of this article, the following example is provided here at the request by Mr. Alec Gindis, as a more specific illustration of the situation when two measures of the same phenomenon supply mutually exclusive results.
Imagine that an American by the name of John went to Europe to visit a friend in Germany, and took with him his Buick. His friend, whose name was Franz, owned an European car, an Audi. They set out on a trip in two cars, whose first leg was from Stutgart to Munich. Buick's odometer was, naturally, graduated in miles, while Audi's odometer was in kilometers. When they arrived in Munich, John read his odometer and found that the distance from Stutgart to Munich was, say, 120 miles. Franz, though, claimed that they traveled 220 kilometers. They realized, of course, that the reason for the two different numbers was simply the utilization of two different scales in their cars. Even though they had no proof that either of the readings was correct, there was also no reason to doubt the readings, as the difference between them was expected, whereas they did not remember the ratio of a mile to a kilometer. Then, though, they continued their trip from Munich to Nuremberg. When they arrived in Nuremberg, John read on his odometer that the distance from Munich to Nuremberg was 107 miles, while Franz read on his odometer that the distance in question was 225 kilometers. Now John and Franz noticed that the two measurements were incompatible. According to Buick's odometer, the distance from Stutgart to Munich (120 miles) was larger than the distance from Munich to Nuremberg (107 miles). According to Audi's odometer, though, the distance from Stutgart to Munich (220 kilometers) was shorter than from Munich to Nuremberg (225 kilometers). Obviously, the two measurements could not be both correct. Objectively, either the distance from Stutgart to Munich is larger, or that from Munich to Nuremberg is larger (as we know for sure that these two distances are not equal). Obviously, at least one of the odometers must be out of order. Since both John and Franz were patriots, John insisted that Audi's odometer was wrong, while Franz was confident that Buick's odometer was to blame. They went to a mechanic who tested both odometers and announced that actually both devices were unreliable, having thus saved the friendship between USA and Germany.
The above example may serve as an illustration designed to clarify the critical comments in regard to WRR's method. This example necessarily involves a certain simplification. More detailed example, which are closer to WRR's actual procedure with the text of Genesis, are given in the following sections of this Appendix.
Example 2. The case of contradictory aggregate measures of a phenomenon
Let us imagine we decided to compare two countries, such as, for example, Canada and Mexico, from the viewpoint of the "proximity" of cities in these countries. Since there are too many cities in each country, making the task of calculating the "proximity" exceedingly time-consuming, we decide to limit ourselves to a certain type of cities, for example, accounting only for the cities with populations of more than 100,000 people. Of course, the threshold of 100,000 is arbitrary, and choosing another threshold could change considerably the outcome of our study.
Next we have to define the "distance" between any two cities. We see at once, that "distance" is an ambiguous concept, as cities are not points on the map. Each city occupies an area, which varies from city to city both in size and shape. We try first to define the "distance" between two cities, as, for example, the distance between the entrances to the city halls of both cities. We discover soon, that the chosen definition is far from being perfect. For example, imagine two cities, 1 and 2, that are stretched as narrow strips along a river. The "endpoint" of the remotest outskirts of city 1 is 60 miles from the nearest to it endpoint of the outskirts of city 2. However, the distance between the entrances to the city halls is, say, 100 miles. On the other hand, there is another pair of cities, 3 and 4, both occupying areas of more or less round shape. The distance between the remotest outskirts of city 3 and the nearest to it outskirts of city 4, is 65 miles, which is larger than for cities 1 and 2, while the distance between the entrances to the city halls of cities 3 and 4 is 75 miles, which is less than for cities 1 and 2. Obviously, the chosen measure of the inter-city distance, namely between the city halls, fails the test of a simple logic. Neither the distance between the "endpoints" of outskirts is logically satisfactory. For people living near that endpoint of city 1 where the straight road starts toward city 2, city 2 is quite close, but for the people living at the opposite end of city 1, the distance to city 2 is quite large. These example shows that the very concept of a "distance" between cities is not quite obvious and simple, and the definition of a "distance" is a matter of choice. That choice, which can be made in many different ways, strongly effects the outcome of the calculation of "proximity."
So far, we had already to make two choices, one being which cities to include into our investigation, and the other how to define the "distance" between any two cities. Actually, we have no a priori proof that "proximity" of cities in a country is an objectively existing property of those countries and can be defined in a non-controversial and single-valued manner.
a) In the case of a text, where the "proximity" of ELS was to be measured, WRR had to make a number of similar arbitrary choices. They chose which ELS to account for and which to ignore. They limited themselves, first, to only what they named "noteworthy" ELS chosen according to the criterion of what they called "domain of minimality" . Second, they limited the ELS to be studied to only those ELS which had a skip length below a certain arbitrarily chosen value, so that the word in question would have not more than 10 of such ELS in the text. Finally, they limited their study only to the words containing between 5 and 8 characters. The reasons for those choices, as they have been given in , had little to do with the objective contents of the "proximity" concept itself. Then, they introduced a very complex definition of a "distance" between two ELS, which was only one of many possible choices, and which in many instances ran against logic and common sense ).
b) As any analogy, our analogy is not complete or perfect. In the case of cities in a country, there may be suggested a rather simple, although far from perfect, way to measure the overall "proximity" of cities by replacing it with another, related measure, that can be defined in an almost unambiguous way, namely as the ratio R of the sum of areas occupied by all the cities in the country, to the total area of that country. The larger is R, the "denser" are situated the cities in that country, hence the "closer" are, overall, the cities of that country to each other. (Of course, the area occupied by a city can be also defined in several different ways. If we agree to allow a certain level of imprecision, it is possible, though, to agree on some criterion as to which areas to include into the cities and which to leave out of consideration).
The described measure R has the advantage of being simple. It has, though, many drawbacks as well. Some of these drawbacks stem from ignoring the role of the absolute size of a country. Indeed, assume that one of the two countries has an overall area ten times as large as the other country. Let's assume that the criterion of "proximity" R, chosen as described above, was found to be about the same for both countries. Obviously, for the two countries in point, this criterion is meaningless. Indeed, let us say, in both countries the area occupied by the cities is 1/3 of the overall area of the country. Then 2/3 of each country's area is "free" from cities. Obviously, in the larger country this "free" area is ten times larger than in the smaller country, and, hence, the distances between the cities are much larger than in the smaller country despite of the equal values of the "criterion" R we chose. Our criterion R implicitly assumed that both countries were of about the same size.
Other drawbacks of the criterion R of "proximity," chosen as described, stem from the fact that calculating this criterion involved an "averaging" procedure, and averaging quite commonly hides many important features of a phenomenon . To illustrate this point, consider two countries of about the same size, for which also the criteria R of "proximity," chosen as described above, were found to have the same value. Let us assume that in one of the two countries, 90% of the cities are concentrated along a sea shore, within an area which constitutes 10% of the overall area of the country, the rest being uninhabitable desert or mountains. In the other country, though, its cities are distributed almost evenly over the country's territory. Obviously, in this case the equal values of the "proximity" criterion R, chosen as described, are of little significance, as in the first country the distances between the cities are much shorter than in the second country. Our criterion R implicitly assumed similar distributions of cities over the countries territories, and when these distributions differ, the described criterion R of "proximity" has very little meaning. Hence, even in a much simpler problem, namely that with cities in a country, the task of defining a meaningful criterion of "proximity" is far from being trivial.
The situation with conceptually related ELS in a text is much worse. Here, an attempt to employ even the imperfect criterion R, described above for the case of cities, would encounter much more serious difficulties. The "area" occupied by an ELS is a much more ambiguous concept than that occupied by a city. Also, whereas all cities in a country are objects of the same nature, in the case of ELS the pairs of ELS related by meaning have to be singled out to measure their "proximity." Hence, to define a single-valued criterion of "proximity" between related ELS is quite a complex task. The results of any choice made cannot be predicted in advance. The choice of a measure of "proximity" can be justified or rejected only by testing the results of its utilization. (This is one more example demonstrating that analogies (even if properly chosen) may be useful for illustration purposes but have no power of proof).
Let us now go back to our example with cities. Even though the situation with the cities in a country is easier to handle than the case of conceptually related ELS in a text, we will, for the sake of an analogy, discuss an example similar to the situation with ELS in a text. To this end we will have to ignore the possibility of choosing the ratio R of areas, as described above, for the estimation of the overall "proximity" of cities, since such a measure can hardly be used for conceptually related ELS. Consider then other ways to estimate the overall "proximity" of cities, ways which can be used also in the case of ELS.
Having chosen a certain definition of the "distance" between two cities, we have now to choose how to estimate the overall "proximity" of the entire multitude of cities. Again, we have here many possible choices. For example, we can choose, as an integral measure of the "proximity," the mean value of the "distance" between all pairs of cities. Alternatively, we can choose for such a measure, for example, the product of all "distances," or any other of many possible combinations of the individual distances between pairs of cities. Since we may feel that there were ambiguous points in the preceding stages of our study, we decide to define more than one measure of the overall "proximity." Let us denote them P1and P2. We expect, of course the value of P1 to be different from that of P2. For example, the mean "distance" between pairs of cities and the product of all "distances" will necessarily be two different numbers. Our goal, though, is not to find certain numbers for the "proximity" in each of the two countries but to find out in which of the two countries the cities are situated "closer" to each other. To do so, we calculate P1 and P2 for both countries and then "rank" them, assigning rank of 1 to the country that has a lower value of P, and rank 2 to the other country.
Let us assume that P1 for Canada turns out to be 0.03 while P1 for Mexico is 0.02. Then, if we rely on P1, we assign rank 1 to Mexico, and rank 2 to Canada. On the other hand, assume that P2 turns out to be 0.04 for Mexico and 0.01 for Canada. Hence, according to P2, we have to assign rank of 1 to Canada, and rank of 2 to Mexico. In other words, if we believe one of our overall measures, say P1, we conclude that the cities in Mexico are situated closer to each other than in Canada. If, though, we decide to believe P2, the opposite conclusion is to be made. These two conclusions are mutually exclusive, they hopelessly contradict each other. At least one of them must be wrong. Then we have no choice but to conclude that either P1 or P2, or, maybe both P1 and P2 are not objective measures of the "proximity" between cities.
There can be several reasons for P1 and P2's failure to reflect an objective property of the countries. One reason can be the improper choice of P1 and/or P2 themselves. Another reason can be the improper choice of the definition of the "distance" between any two cities. One more reason can be that the concept of "proximity" as we have defined it, as an integral, single-valued property of a country, has no real objective contents.
The contradictory outcomes of the application of the two measures in the same test are of a crucial significance, negating any supposedly objective meaning of these measures P1 and P2. (A similar example can be built illustrating the erratic behavior of the four criteria P1- P4 in different tests, by considering a comparison of the cities "proximities" not betwen just two, but among, say, three or more countries).
The results reported by WRR are analogous to what was described in the above example, as it was demonstrated in the body of this article. The only possible interpretation of the results reported by WRR is that the choice of P1, P2, etc, for estimating the "proximity" was unsuccessful, as these P's do not seem to be objective measures of any objectively existing property of texts. Therefore, all the results reported so far by WRR in regard to the ranks of permutations of their data lists are meaningless.
Example 3. One more case of contradictory integral measures of a phenomenon
Let us assume we want to compare men in various countries to judge in which countries men are bigger and in which countries men are smaller than in Ourcountry. As soon as we start designing a method to perform our task, we realize that there is no universally accepted concept of "bigness." We have to introduce one. There are universally agreed upon concepts of, for example, height, weight, shoulder width, foot size, arm length, etc, etc. We have to define "bigness" on the base of those common concepts.
Let us say our first try is to choose height and weight as two measures of "bigness." Then we have to postulate two relationships, one between height and "bigness", and the other between weight and "bigness." The simplest (but not the only one possible) way to do it is to introduce linear dependencies as follows: Bh=KhH and Bw=KwW, where H is height of a man, W is his weight, while Kh and Kw are calibration constants to be defined when we choose methods of measurement of height and weight. Bh and Bw are two values of "bigness," one determined through height and the other through weight of a man. Let us omit the discussion of units to be chosen for "bigness" because ultimately we will anyway use ranks of countries rather than absolute values of "bigness." The two measures of "bigness" do not need to equal each other. What they need to be, is to be compatible. It means that if man X is "bigger" than man Y according to measure Bh, he must be also bigger according to measure Bw.
We start our study with measurements of individual men in various countries. Let us assume we encounter a situation when there is man X whose Bh is larger than for man Y, but whose Bw is smaller than for Y. Who of these two men is bigger? Our test provides no definite answer to this question. Our conclusion is that, at least for these two men, the concept of "bigness" as we defined it, is ambiguous. Hence, with respect to pairs of individual men, the concept of bigness as we defined it is meaningless. At this stage we don't know if the concept of bigness has any objective contents, i.e if there is a logically consistent way to measure bigness via measurements of some other, natural measures of men, such as height, weight, volume, shoulder width, etc etc. It is possible that there is no unambiguous choice of those natural measures which will never contradict each other and provide a single-valued measure of bigness. It is possible that any two natural measures we choose would in some case, even if not always, provide mutually exclusive answers as to which man is bigger (i.e. a man X is taller than man Y, but has a smaller weight than that of Y, or has wider shoulders, but shorter feet, etc).
Remember though that our goal was to analyze the male populations of various countries rather than to compare "bigness" of any two individual men. Therefore, we have to choose certain quantities which would characterize "bigness" of men in statistical sense. We have here a plenty of choices. For example, we can choose mean weight of men as the aggregate measure of their bigness in each country. Or we may choose a cumulative measure of men's bigness as follows. Exclude all men whose weight is below, say 60 pounds, as well as all men whose weight is over 250 pounds. Exclude all men younger than 13, as well as all men older than 85. For each of the rest of men, calculate a function which is as follows: (square "weight multiplied by some coefficient measured in m/kg") plus (square height) plus (square shoulder width) plus (square foot size). Call this function IB, which stands for "individual bigness." By constructing such function, we hope to include into "bigness" several natural characteristics, which would level off discrepancies between, say weight and height, or between shoulder width and foot size, etc). Choosing a combination of several natural characteristics instead of using only one of them seems to be a reasonable way to measure "bigness" in a consistent way. However, the ultimate judgment of whether our IB function reflects an objective characteristic of male population can be done only when the results of measurements are obtained and analyzed in regard to their consistency.
Now we have to choose a cumulative statistical measure of "bigness" for the entire male population of a country. It can be done in many different ways. For example, sum up all the IB's obtained for men in a country, and call the sum P1. To have more than one measure, choose one more aggregate criterion of bigness, for example as the product of all IB's, and call it P2. Then, introduce two more measures of bigness, calculated by the same formulas, but applied to little different, slightly truncated lists of men. Namely, exclude from calculation all men who have lost a limb to an accident or to a surgery. A cumulative measure of "bigness," calculated the same way as P1, but applied to the described truncated list of men, will be denoted P3 , while a measure calculated exactly as P2 but for the truncated list of men, will be denoted P4.
Naturally, since the numbers of men in each country are very large, it is impractical to measure heights, weights, etc, of all men. Therefore we will choose a reasonably big sampling, say, consisting of 10000 men in each country, and measure all four P for them.
When all P are found for a set consisting, say, of 150 countries, we arrange the obtained values of each of four P's in ascending orders. The values of each P for Ourcountry occupy certain places on the four "ladders" of P. If a certain country has the minimum value of a P among all the countries studied, we assign to that country rank 1. The country whose P is the next smallest, is assigned rank of 2, etc. Let us assume Ourcountry has rank r on the ladder of ranks created as described. We peruse the tables of ranks and notice that using four P's resulted in four different ranks of Ourcountry. For example, in the ladder of ranks obtained by using P1, Ourcountry has rank r1, while on the ladder of ranks obtained by using P2, the rank of Ourcountry is r2 , and r1>r2.. At the same time, some country XYZ has a rank below r1 in the list obtained by using P1 but it has a rank higher than r2 on the list obtained by using P2. Then in which country the men are bigger, in Ourcountry or in XYZ? If we rely on P1, we are proud to conclude that the men in Ourcountry are bigger than in XYZ. However, if we rely on P2, our national pride is wounded by the conclusion that the men in XYZ are bigger than in Ourcountry.
Conclusion? One of the following conclusions must be made: 1)There is no such single-valued property of male population as "bigness;" Or 2) the measures such as height, weight, shoulder width, and foot size, are not good choices to measure bigness even if "bigness" could be defined in a logically uncontroversial way; Or 3)Our technique to measure some of those four characteristics was faulty; or 4) Our formula for IB was unnatural and did not reflect "bigness," even if bigness is a meaningful concept; or 5) At least some of our cumulative measures P1-P4 have been meaningless combinations of properties. In other words we will have to conclude that our experiment was a failure.
The above example was as close to what happened in WRR's study as it was practically possible to make. The differences between the above example with "bigness" of men and WRR's measurement of "proximity" are in inconsequential details only. It illustrates the statement that WRR's results are unreliable.
Example 4. The case of a non-existence of a cumulative criterion
Let us imagine that we want to compare, using a certain integral quantity, the religious affiliations of the populations of two countries. A good example would be Yugoslavia before its breakup, vs, say Italy. Would it be possible to define a logically consistent cumulative measure reflecting religious affiliations of those countries' populations? I believe such an aggregate characteristic does not exist. Nevertheless, imagine that an attempt has been made to define such a quantity. Imagine further that a survey has been conducted on samplings of population in each country, which included representatives of Catholics, Orthodox Christians, and Moslems. Each individual was assigned a quantitative value depending on his/her religion. For example, each Catholic would be assigned a value of x, each Orthodox Christian a value of y, and each Moslem a value of z. After all participants in the survey had been accounted for, some cumulative measure P would be calculated, for example a sum of individual "values." Let us assume it has been found that the cumulative quantity for Yugoslavia was P1, and for Italy it was some P2. What is the informative significance of those numbers? None! These numbers do not shed light on anything of consequence and do not add to any knowledge about the countries in question. One may choose any number of other ways to assign "values" to individuals in regard to their religion, but there is no way to make sense of any integral quantity obtained on the base of somehow combining those numbers. The reason for that is obviously the non-existence of a logically consistent single-valued quantity characterizing religious affiliation of people. In the above example the absence of such a cumulative measure was obvious. In some other cases it may be not self-evident, but can often be possible. When the existence of a natural cumulative measure of a phenomenon is not obvious, it may be postulated, but the postulate's validity must be verified by observing the behavior of the postulated cumulative measure.
Example 5. Distributions vs cumulative criteria
The following example can clarify the cognitive power of distributions as compared with cumulative measures. Imagine that we want to compare populations of two countries in regard to the men's height. Let us say that in one of those countries there are two ethnic groups. Men in one of those ethnic groups are typically very tall, while men in the second ethnic group are typically rather short. In the other country there is only one ethnic group. In both countries we choose representative groups of men, each consisting of, say, 10000 men, and we take care to choose the participants in the survey in an unbiased way, i.e. including into the sampling men from all regions of the country, from all age groups, professions, etc. Assume we have found that the mean height of a man is about the same in both counties. What is the informative value of that result? Obviously, rather than shedding light on the question asked, this result actually hides the factual situation. The integral quantity chosen for the evaluation of the men's height, instead of illuminating the problem, provides for a misleading and meaningless conclusion that men in both countries are of about the same height.
The situation changes if instead of a cumulative measure we resort to studying distributions. Seeing the distribution curves of the men over their height, we discover that in one country there are two distinctive groups of men, one short and the other tall; we see what are relative strengths of these two groups; we see also that in the other country men all belong to one group in regard to their height, and, for example, that typically the men in the second country are of an height that is between the typical heights of men in the "tall" and in the "short" groups of the first country, etc. Distributions provide for a manifold material which is much more informative than the aggregate measures can ever be, not to mention that distributions always tell the truth while cumulative quantities often hide it.
When all the above statements have been made, one more question still remains to be answered. It is the question why the "ranks" calculated by WRR have happened to be as small as they are for the non-permuted data list as compared with its multiple permutations. This question is quite apart from the topic of the discussion in this article, which dealt with establishing the validity of the criteria P1, P2. P3, and P4 as objective properties of the texts. The question in regard to the small values of ranks found by WRR for the text of the Book of Genesis has to be answered regardless of the validity of the ranks in question as objective measures of the text's properties. It poses a challenge to one's curiosity. I believe, the "riddle" about small "ranks" of the non-permuted list, which were observed only for the text of Genesis, but not for other texts, has been quite convincingly solved in a number of publications, for example in 10]. It was demonstrated that a slight modification of the data list can cause drastic variations in the measured "ranks." In one such case  where WRR have claimed a rank of 1 for the non-permuted list, a slight modification, by the author of  of the data list, where the modified list appeared to be at least as good as the one used by WRR, and even a more reliable one, the rank of the non-permuted list changed from 1 to 289000. I believe these facts eliminate any need for a further search for explanations of the WRR's extraordinary reports.
1. M. Perakh, posted in this web site (Some Bible-code related experiments and discussions)
2. M. Perakh, posted in this web site ( Do the ELS in the Bible indeed spell what they have been claimed to spell?).
3. M. Perakh, posted on this web site ( Some remarks in regard to D. Witztum's writings concerning the "code" in the book of Genesis).
4. D. Witztum, E. Rips, Y. Rosenberg. Statistical Science, 9, No 3, 429-438, 1994
5. D. Witztum, E. Rips, Y. Rosenberg. This article is posted on Brendan McKay's website.
6. D. Witztum, E. Rips, Y. Rosenberg. Preprint accompanying a presentation to the Israeli Academy of Sciences in 1966 (English translation). It is posted (without appendix) on ( Mark Perakh's website).
7. A. M. Hasofer. This article is posted on this web site (A statistical critique of Witztum et al paper).
8. R.J. Larsen, M.L. Marx. An Introduction to Mathematical Statistics and its Applications. Prentice-Hall, 1986
9. D. Bar-Natan, B. McKay, S. Sternberg. This paper is posted on Brendan McKay's website
10. B. McKay. This paper is posted on Brendan McKay's website.
11. M. Perakh, Surface Technology, 4, 538-564, 1976.