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zashapiro 696 days ago | link | parent

I'm surprised you didn't you mention geometric means. Seems like that would also be a relevant way to look at performance data.

pathdependent 696 days ago | link

Sometimes that's better.

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.


joshfraser 696 days ago | link

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).


saetaes 696 days ago | link

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.


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