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Nicely put, Ann. Many tend to rush past the quality control element of survey analysis and blindly apply "techniques" that some bright spark told them about eons ago. Trouble is, they were also told to "think", to look and listen to the data - because there is always a story to be told.

Thanks for reminding us all.

Thanks, Chuck! And I love and often use the story analogy as well - every data set does indeed have a story to tell. The question is, do you listen ... or simply hack away the inconvenient outliers!

Great post! I would never think of doing such a thing, but I guess another HR practitioner who is not solely dedicated to compensation wouldn't understand the impact of applying this practice.

Thanks, Windsor. Based on the number of times I have encountered people using this technique (some of them compensation specialists even), I'm guessing it is pretty common.

Sorry, Windsor. Being a sucker for a good pun, I just can't resist posting a link to Winsorized Means (http://en.wikipedia.org/wiki/Winsorized_mean), highly relevant to this thread.

for some reason, the link above was defective. this should work better:
http://en.wikipedia.org/wiki/Winsorized_mean

Funny that you mentioned this. I actually do loop off data that skew the particular job family. Most of the time, when such thing is done, it's because there are a few very veteran (employees in the same job for the last 30 years) data points which is skewing the market. But then again, I do have my hands on the data dump/raw data so I could do further analysis. I agree you wouldn't remove outliers willy nilly but if you have a strong case, it can be done.

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About The Author

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    Compensation consultant Ann Bares is the Managing Partner of Altura Consulting Group. Ann has more than 20 years of experience consulting with organizations in the areas of compensation and performance management.

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