I would think so. Alternatively, use the Dunn/Bonferroni test, but it will be a little more conservative. Or you could declare those comparisons to be "planned" or "a priori," like the various tests in a typical factorial ANOVA, and then act like you don't have to worry about inflation of familywise error rate in that case -- which does not really make any sense to me, but is commonly done. IMHO, we worry too much about Type I errors and too little about Type II errors, anyhow.
Karl W. -----Original Message----- From: John Mercer [mailto:[EMAIL PROTECTED] Sent: Tuesday, June 24, 2003 10:46 PM To: [EMAIL PROTECTED] Subject: Re: t-Test vs Dunnett's Test In article <[EMAIL PROTECTED]>, [EMAIL PROTECTED] (Karl L. Wuensch) wrote: > Dunnett's test represents an attempt to control familywise error in > the situation where each of several means is contrasted with a single > reference mean. The test statistic is computed the same as the usual > t (possibly with pooled error), but the function relating t to p is > different, and dependant on the number of groups. So would this be the proper choice if I have a single experimental group and three control groups? I am in this situation, with very large standard deviations caused by uncontrollable factors. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
