From: Glenn Morton (firstname.lastname@example.org)
Date: Sun Dec 15 2002 - 17:07:05 EST
>From: Iain Strachan [mailto:email@example.com]
>Sent: Sunday, December 15, 2002 7:59 PM
>To: firstname.lastname@example.org; email@example.com; Adrian Teo; Glenn
>Subject: RE: Design detection and minimum description length
>>>Generate 100 pairs of (x,y) points from random numbers in the
>>>range 0-1 (this can be done with the Excel RAND() function. Add a
>>>101st (x,y) pair and make it equal to (100,100). Now compute the
>>>correlation coefficient between the two sequences (using the Excel
>>>CORREL() function). You will get an answer for R that is close to
>>>0.999. So your "objective math" is telling you that the sequences
>>>are highly correlated.
>>So???? They are highly correlated.
>So you're telling me that a unit square full of random numbers
>plus the point at (100,100) looks correlated? No a single
>statistician worth his/her salt would support that view. It would
>be regarded as an abuse of statistics.
No, I am telling you that this is irrelevant to the case of DNA where there
is a sequence, not an experimental outlier.
> I see nothing here to support your contention
>>that R is not a good measure of correlation at all.
>Didn't you read the quote from Press et. al?
And you didn't read or understand my objection to your example.
We have a sequence of DNA aaatcggggcac... THere are only 4 symbols. There
isn't an equivalent of 100 in that sequence. There isn't a symbol number
100. Thus your example doesn't fit. Think about this.
This isn't _my_
>contention; it's straight from a standard text book that you will
>find in virtually any university computer science department in
>the world. The assertion is that R is only a good measure of the
>strength of correlation if you already know that a correlation
>exists & that boils down to a knowledge of what the probability
>distributions of the variables look like.
>The remainder of your argument about sequences of bases not
>containing outliers is irrelevant. I never said that they could
>or could not contain outliers. The outlier demonstration was
>simply a specific example I gave in response to your point about
>correlation coefficients, but as usual you try to shift the ground
>of the argument.
The issue in ID is to decide whether or not information in DNA is or isn't
designed. You keep wanting to move away from that issue. Use examples which
are relevant to that issue. Not ones that are irrelevant.
>We could go down the route of talking about HMM's to analyse DNA
>sequences & relate that to Minimum Description Length etc, but I'm
>not interested in engaging in a long point by point debate about
>whether Dembski can detect design or not; you have evidently
>decided a priori that he cannot;
Why is it that design advocates always assume that if someone disagrees with
them it is done on an a priori basis? I have read their books, I have
decided after listening to them. The above charge is simply false.
I on the other hand remain open
>minded and continue to look for ways in which his ideas relate to
>my understanding of machine learning, which has been my academic
>area of research for the last seven years.
Is this an argument from authority? If so, it is a logical fallacy.
Rest assured, if I
>find something in Dembski that can easily be refuted and shown to
>be illogical, within the terms of statistical inference, I'll
>waste no time in shouting about it. I've not found anything yet,
>and nothing you have said has changed my mind; on the contrary, it
>has focused my understanding, that there is nothing wrong with the
>basic maths. It may be that it needs to be developed somewhat; or
>perhaps shown in relation to existing techniques (I have
>identified right from
>the start that MDL is the clear way forward), but I don't find
>anything fundamentally wrong at the moment.
Then you don't understand ID and what it is all about.
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