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Interesting post, and I agree with the conclusion. However, this would not work if the information is presented with a drill-down functionality. Drilling down will effectively reduce the sample size and will magnify the influence of errors/typos.
I frequently have to explain to customers that a report is produced from dirty data and they should be aware of it, to which they would typically remark that a few errors will note make a huge difference, and they are right. But a lot of BI tools allow easy drill-down, and that is where they have to be careful.


Dimitri: Absolutely, drilling down often presents problems, usually because the original research design did not anticipate analysis at those levels. This post does not deal with drilling down, however.


"Another is that statistical techniques by definition generalize the data, and thus are not very sensitive to individual values."

This statement is a little over the top. The robustness of a statistical technique to outliers varies a good deal between methods and across sample sizes. Least squares regression like you're doing here is actually quite sensitive to anomalous data points in many circumstances.

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