Collecting Baseballs Versus Playing Baseball |
(Contrasting Dave Thomas's George Harrison Collection with Serious Code Research)
Physicist Dave Thomas seems to think that collecting baseballs is the same as playing baseball. It's not, but he doesn't appear to understand the difference. And if he doesn't, being as bright as he is, then it's definitely worth clarifying what is going on — for everyone.
In the spring of 2005, Mr. Thomas posted the results of his exhaustive baseball collection effort: the George Harrison collection. His posting is obviously intended to discredit Bible codes as a purported phenomenon. Yet, all he was doing was collecting baseballs (ELSs), not playing baseball. Playing ball involves scientifically investigating Bible code phenomena by staging a fair contest between what chance can produce and what someone claims is the result of encoding.
Before a baseball game begins, it is essential to gather some baseballs to play with. That is what happens when a code search program is run and a collection of short terms are found as ELSs. Now a researcher (or purported debunker) steps up to the plate. To get a hit (i.e., find something highly unusual), a comparison needs to be made between what is expected by chance (i.e., what one would find in a non-encoded text) and what he has observed about the ELSs he is examining. To do that, one needs to know what is expected by chance. Bible code software programs make that easy. When a term is entered in a search program, together with a specified search text and a range of allowable skip sizes, the program tells you how many ELSs (baseballs) you would expect to find. If the number you found is the same as, or close to, what chance would indicate, it's strike one against you.
The problem with Dave Thomas's findings is that he never made such a comparison. So, all he is doing is collecting baseballs, and he's never stepped up to the plate. So, no one should be impressed with his George Harrison collection. All it demonstrates is the result of a protracted exercise in computerized perspiration. How else should one view a search for 108,772 different words from a dictionary file as ELSs? We analogized it as an exercise in draining a lake to get all the fish out, hoping that someone would be impressed with the resulting big pile of stinking fish.
The name of the game in code research is checking for the possible presence of highly improbable events. Here is an example: finding one or more extensions to an ELS that are in good Hebrew (or English, if you are using that context to illustrate something). Thomas found Harrison as an ELS with a skip of 118 letters in the segment of War and Peace he searched. Now, suppose he had checked to see if Harrison was part of an even longer ELS with a skip of 118. So, he gathers the letters that would be part of that longer ELS, picking up every 118th letter before and after the Harrison ELS he had found. Now he is at the plate, about to swing at the ball. If he had found "rocks" right after Harrison, he would have discovered that there was a "Harrison rocks" ELS with a skip of 118 letters. He would have scored a base hit. If he had looked before Harrison to see if he could extend his ELS the other direction, and if he had found "George" there, he would have a "George Harrison rocks" ELS. He would have hit a double. If he had looked further and found "in Liverpool" right after "rocks," he would have hit a triple. Instead, all he did was stay in the dugout and collect baseballs.
Recently, code researcher Moshe Shak found a single ELS consisting of 30 sentences in continuous, good Hebrew. The entire continuous code consisted of 296 letters. That's hitting the ball out of the ball park and into the next county, or perhaps the next state.
There are other ways of getting a hit. Suppose you had found 16 occurrences of a term as an ELS in a given search text with skips in a pre-specified range when less than five were expected by chance. That's such a great variance from what would be expected by chance that you would have scored a hit. It's so different that you would have gotten a double or a triple. A few years ago that happened to us. We searched for Mary ELSs in a passage Christians claim is about Jesus. We found 16 Mary ELSs in Isaiah 52:12–54:3 with skips ranging between one and 50, even though only 4.76 were expected by chance. The odds against this happening by chance are 1 in 25,368. To further verify that we had scored a double or a triple, we permuted (i.e., mixed up the letters of the Mary ELS [MRYM, or Miriam] and ran parallel searches. The alternative searches yielded 6, 4, 4, 9 and 4 matches, respectively, given spellings of MMRY, MMYR, RMMY, MRMY and MYMR). None of these scramblings of Mary occurred a statistically significant number of times in the text. Team Chance struck out while Team Bible Code got a two-base hit.
Here is yet another way of getting a hit. You specify a list of 10 pairs of search terms and find them as ELSs in a search text. Each pair consists of the last name of someone you know and the name of the street on which they live. So, you have pairs like [Jones, Oak] and [Smith, Broadway]. You then determine how far apart each pair of ELSs is, say by defining it as the minimum number of letters between any two of the letters in the pair of ELSs. You total up all these minimum distances. Then, you take your search text and scramble up all the letters, sentence by sentence, and you re-run your search, again recording the total of the minimum distances. You do this 1,000 times, each time recording the total of the minimum distances. Then, you note where the minimum distance from the real text ranks among the minimum distances from all the scramblings. If the real minimum distance was among the 20 shortest out of 1,000, you have hit a single. If it is among the 10 shortest, you hit a double. If among the 5 shortest, you got a triple. If among the two shortest, you hit a home run. This is the kind of thing Witztum, Rips and Rosenberg did in their Famous Rabbi's experiment.
Science involves conducting experiments and/or making comparisons between actual and expected results. That's playing baseball. Collecting baseballs isn't science, and neither is Dave Thomas's effort to make us think that his Harrison collection is scientific.
What are some clear examples of where BCD has played baseball? Go to our posted scientific paper. Look at the graph on the first page. The bell-shaped curve on the left side of the graph shows what Team Chance could produce. The actual result from searching the Bible is marked at the far right of the graph. Team Bible Code hit a home run. Look at Table 1 in that paper. Team Chance is represented by the "Control," and the degree to which Team Bible Code outscored Team Chance is labeled, "Excess in Ezekiel." Now look at Table 3 in the continuation of the scientific paper. The results for Team Chance are shown in the Expected Column and those for Team Bible Code in the Actual Column. Note at the bottom that the number of "at bats" is shown, because it is the batting average that is important, not just simulating over 100,000 at bats on a computer, as Dave Thomas did in order to get a comparatively paltry number of hits from his exhaustive searching.
In a February 2006 posting, Dave Thomas pokes fun at me by presenting a picture of me playing T-ball. He claims that what he did was harder to do than what I was doing. And that is the half truth of it. I was using an easier way of collecting baseballs. But at least I got up to the plate and took a swing at the ball, rather than standing in the dugout gloating over an excessively large collection of baseballs.
Now what of Thomas's allegation? In doing his ELS searches, he required that the entire ELS had to be contained within the two pages of text he had selected. On the other hand, in doing ELS searches, BCD often only requires that at least one letter of the ELS has to appear in the selected two pages of text. ELSs can extend before and/or after the two pages of text. Our way of collecting baseballs is easier than Thomas's. That's obvious. But what does it mean? It just means that we find more ELS matches to a shorter list of search terms. So our searches are very focused topically. There's no reason to believe our way of collecting baseball will result in a better batting average — unless, of course, the text is actually encoded and such codes are topically concentrated.
An illustration: Let's collect 100 ELSs — Thomas's way and our way. Thomas comes up with ELSs for 100 different search terms. We come up with 10 ELSs for each of 10 different search terms. Our terms were pre-selected as being those that are most strongly related to a given topic. Thomas's approach is much more subjective — because it was performed after the fact. What he actually did to get his 100 ELSs was to find 600 ELSs out of a dictionary file and then to only keep those that seemed relevant to his selected topic. He allowed his imagination to run wild and accepted any word that might remotely have anything to do with George Harrison.
Let's compare these two ways of collecting ELSs by drawing an analogy to two methods of gathering 100 baseballs.
Two Ways of Collecting 100 Baseballs
The baseballs gathered by the BCD Method were collected from a more liberally defined search area. That's why we found ten times as many of them. However, the BCD collection is very tightly defined topically — in terms of who's autographs would have to be on them.
In contrast, Dave Thomas's baseballs were gathered from a smaller search area. However, he then accepted any baseball that was autographed — a very liberal acceptance criteria.
So, in summary we have:
So, Thomas criticizes BCD's liberal definition of a collection area, but then fails to acknowledge his own liberal approach to accepting terms as relevant to George Harrison. Thomas accepted one out of every six words (253 out of 1,537) he found as being relevant to George Harrison. Obviously, he exercised frequent stretches of the imagination to claim that, that high a percentage of words were peculiar to the famous Beatle. That's a very liberal way to gather search matches. It bears no resemblance to any of the types of code phenomena presented by serious Bible code researchers. Thomas's George Harrison collection is very long on quantity and very short on quality.
So, we need to look at the entire picture to get the whole truth, not just the first step, from which Thomas derives his half-truth.
In our comparison above, the end result from both searches is 100 baseballs. Note that there is no reason to believe that the baseballs in one collection are better or worst than those in the other collection — in terms of being put into play and resulting in a base hit. Yet that is what counts.
Our procedure makes our searches more focused topically. All that is being done at this point is collecting baseballs. The real game begins when we examine each discovered ELS to see if it is part of a longer ELS. While we were out playing baseball, Dave Thomas was sitting on the dugout bench admiring his collection of baseballs.
The goal of serious, scientific researchers of the phenomenon of Bible codes (highly regarded professors such as Dr. Robert Haralick and Dr. Eliyahu Rips) is radically different and more substantive than Thomas's goal. Their goal is to devise experiments or tests that directly compare the results of code findings with control findings — where the two are as comparable as possible in every way. By so doing, they measure the improbability that a given type of Bible code phenomenon could be due to chance. Dave Thomas, on the other hand, acts as if his job is done if he can produce an ostensibly impressive counter-example — no matter how long or how hard he had to search to find it, and regardless of whether or not his counter-example is directly comparable to a purported code phenomenon. It seems that for Thomas appearance is everything, and substance is irrelevant. Thomas's anti-Bible code postings are quintessential hatchet jobs — not the work of a scientist.
Dave Thomas stops where serious code researchers are just beginning, yet somehow he expects us to be impressed. All he has done is use a dictionary file to milk every possible ELS out of a section of text and then to present the whole disorganized mess. What he fails to appreciate is that most people would much rather watch a baseball game played rather than review a pile of autographed baseballs.
Enjoy finding your own Bible codes.
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