Its been a while since my last post. But I can assure you that this post is not an outlier... or is it?
Identifying outliers in a data set is one of the most difficult tasks we face as problem solvers. Mostly because there are no definitive tests which absolutely identify whether a data point is unique or if it is a natural, expected part of the data set.
Outlier identification reminds us that being a statistical practitioner requires more than a good handle on statistical tools and good knowledge of the process from which the data was collected. Outlier identification requires the ability to use one's mind to take in all this information and make the right decision. Well, at least not make the wrong decision.
The attached is a summary of some methods to look at outliers. It is not a complete compendium of the issue. Please comment below if you have other methods for outlier identification that you have used, or if you feel my presentation needs corrected or adjusted.
Outlier Indentification
Great post John. The PDF attached is a great read for anyone dealing with statistical outliers. I particularly liked your comment that outliers can be investigated, as they may be "good or bad" data points.
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