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Qualitative reasoning in the absence of numerical data is
no vice, and making calculations with data of questionable
relevance just to obtain a number is no virtue.
I was trained as a scientist (some of my old math profs would
argue the point, but never mind that) and have worked most of
my life in science labs or on engineering teams, but what I do
is not science. The quality of the result is better evaluated
through some combination of literary criticism and broad-brush
experimental evaluation performed by watching user tests.
Yes, I know, it can be awfully frustrating to work in a business
that doesn't lend itself to clear-cut definitions of what "good"
means or numerical criteria for what's "good enough."
"Broken English is the official language of science."
Francois Kourilsky, CNRS