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)?
 A: Whether the arithmetic mean is a good measure for a strongly skewed distribution depends on what you are using it for -- what does "good" mean?
One variable that is usually very right skew is income and, usually, you see median income rather than mean income. But why?
Because, usually, when we look at some measure of central tendency, we are looking for something like a "typical" value. So if we wanted to compare incomes in different countries, it would be natural to compare medians (or maybe other quantiles) rather than arithmetic means, because it is closer to what we are interested in. On the other hand, if we were interested in the total capital available in different countries, the arithmetic mean would be better.
Sometimes, though, the arithmetic mean makes very little sense. If we are trying to average numbers on different scales then the arithmetic mean is still BLUE, but it's a BLUE of a number that isn't useful. For instance, suppose you are averaging students' SAT scores (0 to 1600) and their high school grades (0 to 4). Then you won't want the arithmetic mean. Here you want the geometric mean.
Or, if you are averaging rates that have different denominators, the arithmetic mean will again be BLUE, but of a number that isn't useful.  For instance, if you make a trip of 300 km at 100 kph and return at 150 kph, then the arithmetic mean is 125 kph, but this is not very useful because you took 5 hours to go 600 km, which is 120 kph. Here you probably want the harmonic mean.
For more details on this, see my blog post the geometric and harmonic means. 
