I am aware that measures of skewness based on the mean are affected by outliers, and just one outlier can significantly shift the mean. However, in case of a distribution with a very long tail, should I use a mean-based measure of skewness or a median-based measure of skewness, to measure the lack of symmetry of the distribution? Indeed, I am thinking that, probably, long tails might have an effect similar to outliers, by shifting considerably the mean, but I am not sure.
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What you "should" do depends on what you want to find out. It's true that (as you write) a mean based measure of skewnesss is affected by outliers (if they are all on one end). Do you want that? Or not?
This is similar to the case with deciding among mean, median, mode, trimmed mean, etc. It's not that any of them are right or wrong in any particular case (although some elementary stats books may give that idea). It's a question of what suits your particular purposes in your particular case.
It's also similar to choosing a measure of dispersion.
There are also quintile based measures, where you can pick any symmetric quintiles. While quartiles are the most common choice, you could choose any -- again, to suit your case.
Peter Flom
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However, it might be that, in your case, you really need a graphical measure.
– Peter Flom Dec 06 '23 at 15:31