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## Re: Analysing performance data (somewhat OT stat question; sorry)

**From**: |
Mike Miller |

**Subject**: |
Re: Analysing performance data (somewhat OT stat question; sorry) |

**Date**: |
Wed, 22 Sep 2004 23:28:15 -0500 (CDT) |

On Wed, 22 Sep 2004, James Knowles wrote:

`I've been collecting performance data for different software
``configurations, and examining it in Octave.
`

`It's been about 15 year since college stats class, so I'm rusty. Some
``data distributions are normal, and a simple t-test works great for
``testing whether there's a significant difference between configurations.
``Many are visually "obvious," but some are not. They all need to be
``documented, however.
`

`Some are heavily skewed, scrunched up near zero. I don't remember what
``this kind of distribution is called. I do not remember what to use here
``to test a null-hypothesis of the two data populations being the same.
``(Yes, inaccurate terminology; sorry.)
`

`Octave has alot built in, and I don't want to just plug data in
``randomly.
`

`I don't know what your question is. The chi-square distribution on 1
``degree of freedom has infinite density at zero. Maybe you're thinking fo
``that one.
`

`Anyway, the "Mann-Whitney U test," also called "Wilcoxon Rank-Sum test" is
``nice for comparing means when the distributional assumptions of the t test
``are not met. There is not great loss of power when the distributions are
``normal, and there can be very substantial gains in power when the
``distributions are non-normal.
`
Mike
--
Michael B. Miller, Ph.D.
Assistant Professor
Division of Epidemiology and Community Health
and Institute of Human Genetics
University of Minnesota
http://taxa.epi.umn.edu/~mbmiller/
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