Upper bound for the error on the mean for small samples.

I have a sample of an unknown distribution for which I would like an estimate of the mean and the error on that mean. For small samples the typical way of doing this breaks down. What is a reasonable way to estimate the mean and an upper bound of the error on the mean? The error on the mean will go into a fitting method so it is much more important to have an upper bound than an unbiased estimator.

Please include the range this should be applied over.
Not sure what would work best for you. A bootstrap (Monte-carlo) method might be the best way to get bounds if the data are of some odd distribution. Many packages will do that for you. // If you have one or two outliers, you might want to try a trimmed mean (http://en.wikipedia.org/wiki/Truncated_mean). Note that this is not necessarily a good estimate for the actual mean, but may be more reliable than it.


Less is more. Stay pure. Stay poor.
I agree with EdGr's suggestions. Also, it may be informative if you describe what the distribution looks like and the actual small sample size.