My opinion, FWIW: The answer to your question in a strict fashion, assuming the experiment is well designed, depends to a large extent on your "a priori" null hypothesis and how you performed the statistical test.
In this case, presuming that you used a two-sided p value and that you established 0.05 as your p value threshold for accepting/rejecting the null hypothesis, then the p value of 0.009 indicates that the null hypothesis was rejected. Using a two sided p value your a priori null hypothesis would be that there is no difference between the means in "either direction". Thus this p value, stricly speaking, cannot tell you anything about the direction of the difference. If you established an a priori null hypothesis that was direction specific, then you would have to use a one-sided p value to accept or reject that directional null hypothesis. The problem with this approach is that the difference may be contextually significant in the direction opposite the one that your hypothesis is based upon and you may find yourself in a position of strictly having to accept the null hypothesis, since the one-sided p value may be >0.05 in that case. It is for the latter issue, that many folks will not use a one-sided p value in such situations, unless of course a difference in the opposite direction of the null hypothesis is of no consequence. For example, unless a new treatment is better than the current gold standard, you don't care. On the other hand, you may (or should) care if the new treatment is worse than the current gold standard. The danger, in a strict experimental fashion, is to perform the analysis on the data, determine the direction of the difference and then apply the appropriate one-sided test to the data. I have seen this done by others. This is a violation of the basic experimental process, since you already have performed the analysis and have defined a result-based null hypothesis. In my mind, bad form. The null hypothesis should be defined before you know anything about the data. -- Marc Schwartz To Reply Remove "NOSPAM" in E-Mail Address "Dennis Roberts" <[EMAIL PROTECTED]> wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > let's say that you do a simple (well executed) 2 group study ... > treatment/control ... and, are interested in the mean difference ... and > find that a simple t test shows a p value (with mean in favor of treatment) > of .009 > > while it generally seems to be held that such a p value would suggest that > our null model is not likely to be correct (ie, some other alternative > model might make more sense), does it say ANYthing more than that? > > specifically, does the p value in and of itself impute ANY information > about the non null possibilities being in the direction favoring the > treatment group? > > or, just that the null model is not very plausible > > bottom line: is there any value added information imparted from the p value > other than a statement about the null? ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
