As far as I know, the only way to reduce both Type I and Type II error simultaneously is to increase the power. This is usually done by increasing the N, but arguments have been made for increasing power using pooling strategies. In any case, this happens at the design stage, not the analysis stage. At 12:00 PM 12/16/2003 +0100, you wrote: >Message: 3 >Date: Tue, 16 Dec 2003 09:46:50 -0000 >From: "michael watson (IAH-C)" <michael.watson@bbsrc.ac.uk> >Subject: RE: [BioC] ttest or fold change >To: "'Baker, Stephen'" <stephen.baker@umassmed.edu>, > bioconductor@stat.math.ethz.ch >Message-ID: > <20B7EB075F2D4542AFFAF813E98ACD93028224D6@cl- exsrv1.irad.bbsrc.ac.uk> >Content-Type: text/plain; charset="iso-8859-1" > > >This seems small but with a microarray with thousands of genes, this > >easily produces a bunch of false positives. I looked at 10 chips from a > >real control group arbitrarily labeling 5 chips as control and 5 as > >experimental. I would by theory expect 35 false positives and got > >exactly 32, that is 32 sitations in which all the low ranks were in one > >group and the high ranks in the other. For a chip with 22000 genes, you > >would expect 175 false positive results by this criteria. Standard > >statistical methods would give you a specified type I error rate that > >you can count on, it would have found NONE of the genes significant > >(i.e. bonferroni adjustment) > >A truly excellent reply, and one which I will no doubt refer to >frequently; I am still >very much a novice statistician. However, and please correct me if I am >wrong, but >I presume that some scientists are equally afraid of false negatives as >false positives? >i.e. that if we are so conservative such that we try to ENSURE that there >are NO >false positives, we may throw away genes as not differentially expressed >when in >reality they are? It will be interesting to have a discussion on this - >is it possible, >using statistics, to guarentee both no false positives and no false >negatives? If not, >then surely the investigator must decide which is relevant to the study in >question before >going on to decide which stats to use. Eric Blalock, PhD Dept Pharmacology, UKMC 859 323-8033 STATEMENT OF CONFIDENTIALITY The contents of this e-mail message and any attachments are confidential and are intended solely for addressee. The information may also be legally privileged. This transmission is sent in trust, for the sole purpose of delivery to the intended recipient. If you have received this transmission in error, any use, reproduction or dissemination of this transmission is strictly prohibited. If you are not the intended recipient, please immediately notify the sender by reply e-mail or at (859) 323-8033 and delete this message and its attachments, if any.