Kahan summation and relative errors; or real life stories about "getting the wrong result instead of the right one"
I'm interested in "war stories" like this:

I wrote a program involving the sum of floating point numbers, but I did not use Kahan sum.

The amount was
bad_sum
, and the program gave me the wrong result. 
A colleague of mine who is more versed in numerical analysis than I looked at the code and suggested that I use Kahan summation, now the sum
good_sum
, and the program gives me the correct result.
I'm interested in actual production code, not code samples "artificially" created to explain Kahan's summation algorithm.
Specifically, what's the relative error (bad_sumgood_sum)/good_sum
for your application?
Until now, I do not have a similar story. Maybe I'll do some tests (by running my program on the input dataset, running the program results and sums with and without Kahan, estimate the relative error).
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