Subject: Experiments with large N (2) From: Kevin Austin <kevin.austin@xxxxxxxx> Date: Tue, 4 Dec 2007 10:29:03 -0500 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>There are already thousands of examples of "experiments with large N" -- elections for public office. At some point in many elections, the report is ""declared winner" with n% of the polling stations yet to report". Political polls will often report "accurate 19 times out of 20 with a variation of 1.5%". Best Kevin >---------------------------------------------------------------------- > >Date: Mon, 3 Dec 2007 13:42:14 -0500 >From: Robert Zatorre <robert.zatorre@xxxxxxxx> >Subject: Re: Experiments with large N > >Huge samples are very nice if you can get 'em, though such is not always >the case, alas. > >So one thing that I would like to see from people who do have gigantic N >is to do some analyses to determine at what point the data reach some >asymptote. In other words, if you've collected 1,000,000 people, at what >earlier point in your sampling could you have stopped, and come to the >identical conclusions with valid statistics? > >Obviously, the answer to this question will be different for different >types of studies with different types of variance and so forth. But >having the large N allows one to perform this calculation, so that next >time one does a similar study, one could reasonably stop after reaching >a smaller and more manageable sample size. > >Has anybody already done this for those large samples that were recently >discussed? It would be really helpful for those who cannot always >collect such samples. > >Best > >Robert >-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ > >Robert J. Zatorre, Ph.D. >Montreal Neurological Institute >3801 University St. >Montreal, QC Canada H3A 2B4 >phone: 1-514-398-8903 >fax: 1-514-398-1338 >e-mail: robert.zatorre@xxxxxxxx >web site: www.zlab.mcgill.ca > > >------------------------------ > >Date: Mon, 3 Dec 2007 13:58:55 -0500 >From: "J. Devin McAuley" <mcauley@xxxxxxxx> >Subject: Re: Experiments with large N > >This issue nicely highlights the need to report effect size measures. With a >large enough sample, even the smallest of effects will show up as reliable! >:) > >Best regards, >Devin > >