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Re: Real-Life

To: Brian S Cade <brian_cade@usgs.gov>
Subject: Re: Real-Life
From: Spencer Graves <spencer.graves@PDF.COM>
Date: Fri, 27 Jun 2003 07:38:53 -0700
Cc: Spencer Graves <spencer.graves@PDF.COM>, s-news@lists.biostat.wustl.edu, s-news-owner@lists.biostat.wustl.edu, Jim Stapleton <stapleton@stt.msu.edu>
References: <OF38218B82.0B7CA662-ON87256D52.004D0140@cr.usgs.gov>
User-agent: Mozilla/5.0 (Windows; U; Windows NT 5.0; en-US; rv:1.0.2) Gecko/20030208 Netscape/7.02
Yes, definitely for tolerance intervals, you need a deeper understanding of the distribution. Nonparametric tolerance invervals require much larger samples to produce tight and stable results than do parametric tolerance interval procedures -- and the validity will be only as good as the parametric assumptions.

          Thanks for that counterexample.

Best Wishes,
Spencer Graves

Brian S Cade wrote:
Perhaps the important thing to keep in mind here is that our data can often
deviate alot from normality yet inferences about means, changes in means,
etc. will not be too far astray.  But start asking questions that require
estimating other statistics like percentiles (which are needed to estimate
prediction intervals for individual units or to estimate tolerance
intervals for a proportion of the population) then deviations from
normality can make a big difference.  My experience is that many
biological/ecological questions would be more realistically solved by
tolerance interval estimates - how does some large (e.g, 90%) proportion of
the population respond.


Brian S. Cade

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  brian_cade@usgs.gov
tel:  970 226-9326





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