I think the more important point is that knowledge of the underlying distribution -- normal (Gaussian), log-normal, exponential, power law, Weibull, etc -- is very important. The more skewed the underlying distribution, the less relevant the mean becomes -- and it can cause you to make some very bad inferences.
Great point. While geometric means are a little tricker for people to understand, they are definitely one of the better ways of looking at performance data since they describe the central tendency of the data. I personally prefer using the 95th or 99th percentiles because they force you to keep the mindset that you need to be fast for everyone (sans outliers of course).
Agreed, it's a great alternative to mean for performance data - Keynote even has it as an optional aggregation function when viewing data. Heck, if you have your performance data in a MySQL database, it's as simple as 'select exp(avg(ln(myVal)))', and doesn't require you to install a UDF, as you would if you wanted to find the median.
