# benchmarking a right skewed distribution

I'm benchmarking procedure groupings done in a hospital and all the items associated with the cost of the procedure. As you can imagine the range can be from .01 to 50k dollars per item. With the majority of the procedures being in the 100−2000 range the distribution spikes and drops very quickly with the tail trailing to the right.

I've experimented with using the geometric mean removing outliers with the standard deviation 1.96 above and below the geometric mean.

As well as using the median and removing the 5th and 95th percentiles.

When I state benchmark I'm referring to a cost a physician should try to meet. The benchmark is more of a target and not a range. Currently i'm using the geometric mean for that "target".

What other methods are there to benchmark a right skewed distribution cost on a large amount of data over 29 million rows of data with a range between \$1 and 3m?

• How is this unclear? I'm asking what other techniques are available other than geometric mean and median for benchmarking a right skewed distribution? – user1470034 Feb 8 at 13:44
• The problem might be the use of domain-specific argot to express the question: it's possible nobody outside of that domain feels they truly understand what you're talking about. Could you explain what a "cost bucket" is, what it means to "benchmark" one, what an "event level" might be, and how to quantify how good a benchmark might be? – whuber Feb 9 at 18:21
• Ok, i rewarded it. Is that better? – user1470034 Feb 10 at 18:09
• It is better, thank you -- but could you explain what "benchmarking" amounts to? This is not a standard statistical term. – whuber Feb 10 at 22:25
• Added a definition of benchmarking. – user1470034 Feb 10 at 23:30