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Dimitri

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.

Kaiser

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.

J

"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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