# [BioC] Multtest Question!

**Sandrine Dudoit
**
sandrine@stat.berkeley.edu

*Tue, 11 Feb 2003 09:55:43 -0800 (PST)*

We used Gaussian p-values simply as an example.
You can use the function mt.maxT to obtained permutation unadjusted
p-values as well as adjusted p-values.
In general, the Gaussian p-values should be adequate when you have
fairly large sample sizes in both groups, so that AVERAGES in both
groups are approximately normally distributed.
The assumptions behind t-distribution p-values are more restrictive:
it is assumed that INDIVIDUAL expression measures have a Gaussian
distribution. This is not very realistic.
You can find a discussion of t and z-tests in pretty much any
introductory statistics textbook. I like "Statistics" by Freedman,
Pisani and Purves.
Best regards,
Sandrine
-------------------------------------------------------------------------------
Sandrine Dudoit, Ph.D. E-mail: sandrine@stat.berkeley.edu
Assistant Professor Tel: (510) 643-1108
Division of Biostatistics Fax: (510) 643-5163
School of Public Health http://www.stat.berkeley.edu/~sandrine
University of California, Berkeley
140 Earl Warren Hall, #7360
Berkeley, CA 94720-7360
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On Tue, 11 Feb 2003, Puhong Gao wrote:
>* Hi, all,
*>*
*>* In the documentation of the Multtest R package, the raw p values were generated based on the standard Gaussian distribution. Can someone explain to me why it should be like that? What situations can it be applicable to? The p values based on the standard Gaussian distribution are much smaller than those based on the t-distributions. The consequence of this is more genes will be picked up as significant. How can this be explained? Your inputs will be very appreciated.
*>*
*>* Have a great day,
*>*
*>* Puhong Gao, PhD
*>* Dept. of Developmental Psychobiology
*>* NYSPI/Columbia University
*>* 1051 Riverside Drive
*>* New York, NY 10032
*>* (212) 543-5710
*>*
*>* [[alternate HTML version deleted]]
*>*
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