On 17 Dec 2003 15:54:09 -0800, [EMAIL PROTECTED] (Doug) wrote: > Hi, > > I was sitting through a presentation of some research yesterday about > the results of a pilot study where a control group was compared with a > treatment group and I found that I was rather confused about the use > of the term standard error. The researcher appeared to be saying > something about having too small a standard error was not a good > thing. > > Could someone please explain or link me to a page where I could get > some detailed information about use of the standard error. I think > that it is a measure of the standard deviation of the sample means,
- you are correct, that is *one* of the uses - > but this doesn't appear to be the context that was used (from my > understanding). - and it could be the standard deviation of some other statistic. > > When is it good to have a large standard error? When is it good to > have a small standard error? Do these answers differ for different > tests and different experimental designs? The replies posted so far seemed fine. I hope that my re-stating of the message may add something. It is *great* to achieve a small standard error, as a result of good experimental design. It is *bad* to have a statistical analysis produce a standard error that you know is too small to be realistic. That makes you ask, "What went wrong?" - It is apt to indicate overfitting, when you see it in a pilot sample that is too small for the analysis that is intended for a larger sample. When you have a *huge* sample - is the other case where the SE becomes "too small". That is, it provides a thumbnail-test that might tell you that *something* could be happening; but there are two problems that could lead to the conclusion that the term is 'too small.' - One, it could falsely imply a difference, if this is not a randomized trial, and the assumption of independent sampling has (merely) come out to be untrue. (In other words, you ought to (a) control for confounding variables, and perhaps (b) use a bigger error term.) - Two, it could show that the nominal p-level that you are using is inappropriate for the size of effects that you were interested in. If that's the case, you should adjust the p-level. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
