> I am not a statistician, so I don't know under what circumstances outliers are usually thrown out.

As an industrial statistician, I can tell you: way too often.

Outliers are the signal among the noise. They indicate something. It is nearly always worth finding out what, instead of removing them. If they indicate a flaw with measurement or the process, then fix that flaw and re-do the measurement or re-run the process. Outlier gone! But in a much more informative way.

I have absolutely no knowledge of this stuff (in particular of your industry), but I would have thought that most outliers indicate either a measurement error (which you suggest) or a cause that hasn't been taken into account. For the latter, I'm thinking for example about countries whose life expectancy is completely out of line with their per capita income, or some such. Do unexpected causes appear to cause outliers in your field?