In article <[EMAIL PROTECTED]>, dennis roberts <[EMAIL PROTECTED]> wrote:
>in the moore and mccabe book (IPS), in the section on testing for >differences in population proportions, when it comes to doing a 'z' test >for significance, they argue for (and say this is commonly done) that the >standard error for the difference in proportions formula should be a POOLED >one ... > >in their discussion of differences in means ... they present FIRST the NON >pooled version of the standard error and that is there preferred way to >build CIs and do t tests ... though they also bring in later the pooled >version as a later topic (and of course if we KNEW that populations had the >same variances, then the pooled version would be useful) > >it seems to me that this same logic should hold in the case of differences >in proportions The difference is that when dealing with real data, it is possible for two populations to have the same mean (as assumed by the null), but different variances. In contrast, when dealing with binary data, if the means are the same in the two populations, the variances must necessarily be the same as well. So one can argue on this basis that the distribution of the p-values if the null is true will be close to correct when using the pooled estimate (apart from the use of a normal approximation, etc.) Radford Neal ---------------------------------------------------------------------------- Radford M. Neal [EMAIL PROTECTED] Dept. of Statistics and Dept. of Computer Science [EMAIL PROTECTED] University of Toronto http://www.cs.utoronto.ca/~radford ---------------------------------------------------------------------------- ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
