# How is that possible that simple arithmetic mean works well even for strongly skewed distribution?

I was taught, that the arithmetic mean is sensitive to outliers and skewness. This was natural to me - the observations lying far from the "central point" of the distribution "pull" the measure towards them. Then I was advised to use the arithmetic mean only for symmetric distributions, ideally normal, but symmetry was found as "good enough". When it came to asymmetric distributions, left or right, I was told to use the median. In case of log-normal distributed data, which - as I was told - have multiplicative nature (whatever it means), I was advised to use the geometric mean, as better measure of the central tendency. But then I was told, that the arithmetic means is "valid for any distribution", as it is the BLUE (best linear unbiased) estimator of the population expected value (still - the arithmetic mean).

Now I'm lost. If the arithmetic mean is so good measure even for so strongly skewed distributions as the log-normal, then why telling about "robust measures", like geometric mean, winsorized mean, trimmed mean, mid-mean? According to what I was told, only the arithmetic mean is the BEST estimator. Moreover, the geometric mean is not an estimator of the population expected value, so why even bother with? Is this because the expected value, the first raw moment, not always is the best measure characterizing the central tendency? And that is because, even if the arithmetic mean is best estimator, we instead propose the geometric mean as the best measure of the central tendency instead (not expectance) instead?

In other words: is it that however every distribution (for which the first raw moment is deifned) has the expected value, which is the arithmetic mean, but the expected value is "useful" only for the normal (or symmetric) distribution, and for any other skewed ones we may prefer different measure of central tendency instead of the expectance (which plays important role in theoretical considerations anyway)?

• useful for what purpose? moments are always useful for something Jan 31, 2020 at 20:58
• You might want to define "well" when you ask if it works well. Jan 31, 2020 at 20:59
• The sample mean is a good estimator of the true mean (if it exists) but the true mean is not always a good measure of central tendency. Jan 31, 2020 at 21:31
• Thank you, Michael M.! That is it! So the dozens of central tendency measures (Wikipedia lists more than ten of them, including the Winsorized mean) are invented to better describe the "typical observations". For symmetric distributions, including the normal one, the central tendency also coincides with the arithmetic mean, the first raw moment, right? Jan 31, 2020 at 21:56
• 1. The sample mean can be an excellent estimator of the population mean when the distribution is moderately skew (consider the exponential, for example; it's maximum likelihood in that case). 2. One thing I tend to find when people try things with the lognormal is that they restrict attention to quite "nice" cases. You might look at how things go when the skewness is actually high - like say $\sigma=4$. Feb 1, 2020 at 6:16