While this joke is amusing, it makes an excellent point
about statistics, and it was one I couldn’t overlook when viewing the Pros and
Cons of NAFTA. If manipulated or presented in a certain way, numbers can say
whatever you want them to say. This seemed very apparent when talking about the
Pro’s for NAFTA that I saw in the article about its effects on Canada. They
talk about the trillions Canada sold to the United States, the millions of jobs
being created, and the billions in surplus that is allegedly a direct result of
NAFTA. What they do not mention in this discussion is, as the article says “the
quality and stability of those jobs.” As mentioned, 40 percent of the jobs
created are only part time jobs and 17 percent of job growth is credited to
self-employment. That means that over half of the jobs NAFTA supporters claim
that is has created are, quite frankly, flukes that do not reflect their
viewpoint in a fair and balanced way. Also, while they claim that all this
money is pouring into Canada, the country still seems to be struggling
economically and making massive cuts, so where is exactly is this money going? That’s
not an answer NAFTA supporters seem interested in answering. Do I think NAFTA
is all bad and deserves the bum rap it gets? No, probably not. After all, there
is a lot to be said for “With China’s new found economic power, these problems
would have occurred anyway.” However, it bothers me when people just throw out numbers
to prove their points without any sort of interpretation of that “facts.” The fact is, numbers in the right context can
prove whatever you want.
Monday, July 16, 2012
"The man's a genius; he could disprove gravity"- Aaron Eckhart, "Thank You For Smoking"
A mathematician, applied mathematician and a statistician
all apply for the same job. At the interview they are asked the question, what
is 1+1. The mathematician replies, "I can prove that it exists but not
that it is unique." The applied mathematician after some thought replies,
"the answer is approximately 1.99 with an error in the region of
0.01." The statistician steps outside the room, mulls it over for several
minutes, and eventually in desperation returns and inquires, "so what do
you want it to be?"
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