I want to estimate a confidence interval for the mean of a metric variable that's extremely skewed to the right. As I (hopefully!) understood, the central limit theorem will make sure that the sampling distribution of the mean will follow a Gaussian distribution (assuming enough observations). However, the skewed distribution causes the standard deviation to be very large compared to the mean value, rendering a very wide confidence interval that's not too informative.
Is there any way (e.g. by a transformation or something) to obtain a smaller interval?
Ruben van den Berg
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