RE: Design detection and minimum description length

From: Iain Strachan (
Date: Fri Dec 15 2000 - 19:59:29 EST

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    >>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.

      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? 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

    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; 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. 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 fr!
    the start that MDL is the clear way forward), but I don't find
    anything fundamentally wrong at the moment.


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