Twin Telepathy and the Illusion of Separation

[Paul C. Anagnostopoulos]

Originally Posted by Craig Weiler

I find this situation with the twins interesting because absolutely no theory that depends on an objective universe is truly adequate to explain what is going on.

Could you show your calculation of the probability of this happening by sheer chance plus some genetic disposition?

~~ Paul

[Iyace]

Originally Posted by Paul C. Anagnostopoulos

Could you show your calculation of the probability of this happening by sheer chance plus some genetic disposition?

~~ Paul

Most Common Male First Names in the United States

(1/1219)*(1/1219)*(1/4275)*(1/4275)*(1/4275)*(1/4275)*(1/1219)*(1/1219)*(1/4275)*(1/4275)*(1/1000 (Lets say there's 1000 different types of jobs. Theres more, but lets say that there is.))*(1/1000)*(1/999)*(1/999)*(1/21900)*(1/21900)*(1/1200 ( Lets say there's 1200 different diseases out there.)*(1/1200)=

0.000000000000000000000000000000000000000000000000 000000000000107643241

Multiplication Rule of Probability

(1/1219) = the chance to be named jim, as per the names list above. The chance to be named 'jim' is not a genetic factor. Not enough data, however, is presented to state whether or not their names were given at the time of their birth, or not.

(1/4275) = The chance of marrying a woman by a certain name, as per the names list above. Again, this is not a genetic factor

(1/4275) = The chance of re-marrying a woman by a certain name. You could, in theory, multiply be divorce rate, which would decrease the chance, but I chose not to out of mercy.

(1/1219) = The chance of having a son by the same first name, as per the names list.

(1/4275) = The chance of having a son and giving him a certain middle name, as per the names list.

(1/1000) = The chance of having a certain type of job. This needs to be adjusted by frequency of jobs, but I'm also assuming that 1 police officer for 1000 citizens sounds reasonable. However, as of 2006, there are 800,000 How many police officers are employed in the United states

police officers and 300 million citizens in the us, making that true statistic 0.00266666667. However, this is a statistic much after the current surge of officers, and including federal agents, so for the sake of clarity, the statistic I used is slightly lower. You may use the true statistic today if you wish to recalculate.

(1/999) = the chance of being a firefighter. Per U.S. Fire Administration | Working for a Fire-Safe America
, there is a 0.000927666667 chance of being a firefighter, so the real statistic is actually lower than the one I provided. Again, I did it for the sake of clarity, and mercy.

(1/21900) = Provided the jims lived 60 years, then that is the chance of them dying on a particular day in that their lives. However, this statistic needs to be adjusted for age, but keep in mind that 60 years old is much lower than life expectancy, and I don't know at what age they died. I imagine if they died older than 60 (plausible), then you wouldnt have to adjust this statistic this much.

(1/1200) = Again, I'm only assuming there is that many diseases out there. The article didnt say what the disease was, so I don't know whether or not it was genetic. However, adjusting for the increase of diseases, and their smoking habits, I think this statistic would be much much lower. Feel free to provide a weighted calculation.

The chance of it happening is one in 9.28994696 10^60.

[Kamarling]

Robert Anton Wilson The New Inquisition pt 2 - YouTube

About 4:00 minutes in.

[fls]

Originally Posted by Iyace

Most Common Male First Names in the United States

(1/1219)*(1/1219)*(1/4275)*(1/4275)*(1/4275)*(1/4275)*(1/1219)*(1/1219)*(1/4275)*(1/4275)*(1/1000 (Lets say there's 1000 different types of jobs. Theres more, but lets say that there is.))*(1/1000)*(1/999)*(1/999)*(1/21900)*(1/21900)*(1/1200 ( Lets say there's 1200 different diseases out there.)*(1/1200)=

0.000000000000000000000000000000000000000000000000 000000000000107643241

Multiplication Rule of Probability

(1/1219) = the chance to be named jim, as per the names list above. The chance to be named 'jim' is not a genetic factor. Not enough data, however, is presented to state whether or not their names were given at the time of their birth, or not.

(1/4275) = The chance of marrying a woman by a certain name, as per the names list above. Again, this is not a genetic factor

(1/4275) = The chance of re-marrying a woman by a certain name. You could, in theory, multiply be divorce rate, which would decrease the chance, but I chose not to out of mercy.

(1/1219) = The chance of having a son by the same first name, as per the names list.

(1/4275) = The chance of having a son and giving him a certain middle name, as per the names list.

(1/1000) = The chance of having a certain type of job. This needs to be adjusted by frequency of jobs, but I'm also assuming that 1 police officer for 1000 citizens sounds reasonable. However, as of 2006, there are 800,000 How many police officers are employed in the United states

police officers and 300 million citizens in the us, making that true statistic 0.00266666667. However, this is a statistic much after the current surge of officers, and including federal agents, so for the sake of clarity, the statistic I used is slightly lower. You may use the true statistic today if you wish to recalculate.

(1/999) = the chance of being a firefighter. Per U.S. Fire Administration | Working for a Fire-Safe America
, there is a 0.000927666667 chance of being a firefighter, so the real statistic is actually lower than the one I provided. Again, I did it for the sake of clarity, and mercy.

(1/21900) = Provided the jims lived 60 years, then that is the chance of them dying on a particular day in that their lives. However, this statistic needs to be adjusted for age, but keep in mind that 60 years old is much lower than life expectancy, and I don't know at what age they died. I imagine if they died older than 60 (plausible), then you wouldnt have to adjust this statistic this much.

(1/1200) = Again, I'm only assuming there is that many diseases out there. The article didnt say what the disease was, so I don't know whether or not it was genetic. However, adjusting for the increase of diseases, and their smoking habits, I think this statistic would be much much lower. Feel free to provide a weighted calculation.

The chance of it happening is one in 9.28994696 10^60.

You are going at it backwards. None of these were specified a priori, so those probabilities are irrelevant. For example, it could have been their daughters who were named after their mothers, and they both could have had pet hamsters named Chuck. Instead, out of hundreds of pieces of information which describe a life, those dozen which matched were listed. So the question really is, as Paul put it, how many would you expect to match were you to perform this exercise on unconnected but similar pairs (say, two babies born in the same hospital, on the same day, but to unrelated families)? From that you can figure out whether this proportion is an outlier (excluding genetic predispositions, such as height and weight, migraines, etc.).

Linda

[Interesting Ian]

Originally Posted by Paul C. Anagnostopoulos

How many sets of twins separated at birth don't have a big pile of coincidences?

~~ Paul

Not quadrillions which is something like we'd need.

[Interesting Ian]

Originally Posted by Iyace

Most Common Male First Names in the United States

(1/1219)*(1/1219)*(1/4275)*(1/4275)*(1/4275)*(1/4275)*(1/1219)*(1/1219)*(1/4275)*(1/4275)*(1/1000 (Lets say there's 1000 different types of jobs. Theres more, but lets say that there is.))*(1/1000)*(1/999)*(1/999)*(1/21900)*(1/21900)*(1/1200 ( Lets say there's 1200 different diseases out there.)*(1/1200)=

0.000000000000000000000000000000000000000000000000 000000000000107643241

Multiplication Rule of Probability

(1/1219) = the chance to be named jim, as per the names list above. The chance to be named 'jim' is not a genetic factor. Not enough data, however, is presented to state whether or not their names were given at the time of their birth, or not.

(1/4275) = The chance of marrying a woman by a certain name, as per the names list above. Again, this is not a genetic factor

(1/4275) = The chance of re-marrying a woman by a certain name. You could, in theory, multiply be divorce rate, which would decrease the chance, but I chose not to out of mercy.

(1/1219) = The chance of having a son by the same first name, as per the names list.

(1/4275) = The chance of having a son and giving him a certain middle name, as per the names list.

(1/1000) = The chance of having a certain type of job. This needs to be adjusted by frequency of jobs, but I'm also assuming that 1 police officer for 1000 citizens sounds reasonable. However, as of 2006, there are 800,000 How many police officers are employed in the United states

police officers and 300 million citizens in the us, making that true statistic 0.00266666667. However, this is a statistic much after the current surge of officers, and including federal agents, so for the sake of clarity, the statistic I used is slightly lower. You may use the true statistic today if you wish to recalculate.

(1/999) = the chance of being a firefighter. Per U.S. Fire Administration | Working for a Fire-Safe America
, there is a 0.000927666667 chance of being a firefighter, so the real statistic is actually lower than the one I provided. Again, I did it for the sake of clarity, and mercy.

(1/21900) = Provided the jims lived 60 years, then that is the chance of them dying on a particular day in that their lives. However, this statistic needs to be adjusted for age, but keep in mind that 60 years old is much lower than life expectancy, and I don't know at what age they died. I imagine if they died older than 60 (plausible), then you wouldnt have to adjust this statistic this much.

(1/1200) = Again, I'm only assuming there is that many diseases out there. The article didnt say what the disease was, so I don't know whether or not it was genetic. However, adjusting for the increase of diseases, and their smoking habits, I think this statistic would be much much lower. Feel free to provide a weighted calculation.

The chance of it happening is one in 9.28994696 10^60.

We can't calculate it like this. We can't just pick the aspects which match. If both me and you had a pair of dice and continually threw them and added up the scores, then occasionally we will get the same scores. But it would be an error to calculate the probabilities of each match and multiply them, because we're ignoring all the misses!

[Interesting Ian]

Related to this chance hypothesis back in the late 19th Century there was a “Census of Hallucinations” carried out by Edmund Gurney to assess the chance-coincidence hypothesis of crisis hallucinations.

Read here about 2/3rds down.

The Census had netted 350 examples of recognized visual hallucinations of living persons known to the percipients and believed by them up to that moment to be still alive (only percipients who had had no other hallucination were included). Of these 80 were death-coincidences in that they coincided within twelve hours either way with the death of the recognized individual. However it was apparent from the distribution of reported cases over time that people were much more likely to forget noncoincidental cases than coincidental ones. The Committee calculated that (after elimination of all cases occurring to percipients under the age of ten, and of all cases which might be regarded as doubtfully hallucinatory or otherwise suspect), the real number of recognised visual hallucinations could be reckoned at about 1,300 and (to be on the safe side) the number of death-coincidences at 30. They then argued that since the Registrar General’s tables showed the chance of any person taken at random dying on a given day was 1 in 19,000, the chance of any given single event, such as someone’s having a one-off hallucination of an individual known to him, coinciding with the death of that individual would also be 1 in 19,000. The actual proportion of such coincident hallucinations was about 30 in 1,300, or 440 times the predicted figure. In most of the residual 30 cases the percipients had been interviewed by members of the committee or their representatives, and the collector had no previous knowledge of the respondent’s experience. In at least 16 cases the percipient either had no reason to suppose that the decedent was unwell or no reason to suppose that his or her illness should occasion anxiety, nor were the figures seen predominantly those of elderly and presumably more vulnerable persons (A. Johnson, Review of Ueber die Trugwahrnehmung, by Edmund Parish, in Proceedings of the Society for Psychical Research, vol. 11, 1895, pp. 170-171; PSPR on Lexscien: Library of Exploratory Science). Occasional errors of memory came to light but were in general not such as to invalidate the basic fact of the correlation in time between hallucination and death. In a sprinkling of cases percipients had made contemporary notes of the experience, or had mentioned it to other persons before learning of the death.

[Paul C. Anagnostopoulos]

Originally Posted by Kamarling

Robert Anton Wilson The New Inquisition pt 2 - YouTube

About 4:00 minutes in.

So are you suggesting that probability has nothing to do with it? That somehow twins have access to a magical world in which coincidences "just happen"?

~~ Paul

[Paul C. Anagnostopoulos]

Originally Posted by fls

(excluding genetic predispositions, such as height and weight, migraines, etc.).

And the tendency to enjoy certain jobs, biting nails, smoking, enjoying the taste of beer.

And, of course, love of Chevies.

~~ Paul

[fls]

Take the Birthday Problem as an illustrative example.

Let's say we are in a room with 23 people and it happens that two share the same birthday. According to Iyace, the probability this would occur is 1/365 * 1/365 or 0.00075 % - highly improbable. Yet this is a well known problem whose probability is actually 50% (Birthday problem - Wikipedia, the free encyclopedia). This demonstrates that the tendency here to figure probabilities by multiplying the a priori probabilities of events is very wrong.

Linda