Some years ago I investigated errors generated by people collecting environmental data while out in the field. In those days we
had computers and databases back at the lab, but field data was collected
manually.
I’d written a range of error-trapping routines to pick up
errors during data input at the lab so all I had to do was link errors to
people. The survey included several hundred field workers and hundreds of
thousands of items of data. These were not major errors by the way, but they
had to be corrected.
Findings
I suppose I was most surprised at how many errors were being
made and how consistent each person’s error rates were.
Firstly, line managers working in the field to keep their
hand in. They tended to generate more errors than anyone else. Many should have
been locked in their offices and never allowed into the field under any
circumstances.
Secondly, there were a few people who were extremely
meticulous, making a very small number of errors day in day out, but there were
not many like that – maybe five or six at most.
Thirdly, there were people at the other end of the spectrum
who routinely made a large number of errors.
So – not particularly surprising really, but what did strike
me was the difference between the best and the worst. The worst field workers regularly
made at least twenty times as many errors as the best.
Yet that did not mean that the worst couldn’t care less
about the work – far from it as far as I could see. People doing environmental
field work tend to be interested and conscientious.
I don’t know what became of the survey in the longer term,
because I moved on. My reports were greeted with surprise and not a great deal
of enthusiasm, but I always remember just how consistent people are when it
comes to making mistakes in largely routine work.
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2 comments:
Knowing the error rate, would you have been able to correct for it?
Sackers - not usually. Some of it was simply missing data but where data was obviously wrong we had to go back to the field worker.
What we couldn't detect at all was data which was wrong but plausible.
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