From: John Conover <john@email.johncon.com>

Subject: Re: Judgment, Evaluation, Feedback, etc. LO9962

Date: Sun, 15 Sep 1996 19:52:59 -0700

John Constantine writes: > Replying to LO9928 -- > > Eric Bohlman speaks eloquently regarding the damned bell curve > requirements which can inflict such pain on an organization by > necessitating that there be some "bad" among the "good". As mentioned in > another post (Thomas Benjamin) I offered that there are usually few good > metrics which would provide justification (quantifiable) for telling > someone across the desk that they are "bad" in relation to "our > standards", when the standards themselves are flawed in the first place. > Which "bell curve" are you referencing? Those that have a standard deviation that adds linearly, (ie., Brownian fixed increment fractals, which are very common,) or, maybe, one that adds root mean square, (ie., Gaussian, or Normal, which, also, are very common)[1]? There is an interpretational difference. Many statistical variations have characteristic distributions that look like "bell curves," (differing only by the kurtosis of the tails of the curve.) The interpretation of "good" and "bad," (whatever those two terms mean,) is subjective, and not objective without clarifying the mechanism of the underlying statistical process[2]. Without this clarification, any interpretation is nothing but "contemporary numerology. [see same reference.]" Actually, there are infinitely many "bell curves," all of which must be interpreted differently [see first reference]. Such simple classifications as "good" and "bad" have little meaning in formal statistics-which, if I am not mistaken, is the essence of your posting. Just a formal clarification. John [1] "Fractals, Chaos, Power Laws," Manfred Schroeder, W. H. Freeman and Company New York, New York, 1991, ISBN 0-7167-2136-8, pp. 157. [2] "Searching for Certainty," John L. Casti, William Morrow, New York, New York, 1990, ISBN 0-688-08980-1, pp. 201. BTW, just as a tutorial note, two dice at a crap table exhibit a Normal, or Gaussian, distribution of the variance of numbers on the dice, over many throws. The gambler's capital, (that is playing craps,) exhibits a variance that is fractal in character. The variance of the dice can not be optimized and exploited by a gambler. The variance in the gambler's capital can. Billions have been lost on schemes that attempt to optimize the "dice" on Wall Street, as opposed to optimizing the wagering strategy of a portfolio. A very subtile difference, indeed. Both variances have "bell curve" distributions. Ask any programmed trader, or see reference 1, pp. 128. -- John Conover, john@email.johncon.com, http://www.johncon.com/

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