The Texas sharpshooter fallacy is
when differences in data are ignored, but similarities are stressed, leading to
a false conclusion that is inferred. This fallacy allows people to almost
ignore the conflicting date in favor of the few pieces of information that
actually fit together, and therefor are focused upon and blown out of
proportion to fit the author/ arguers main points. This fallacy can be seen
often when a person has collected a large amount of data that may mostly conflict,
but he/she decides to specifically focus on a small subset of that data that
they find most closely relating to what they were trying to prove in the
collection of the data. A visual example of this fallacy can be explained like
this: “A Texan who fires some gunshots at the side of a barn, then paints a
target centered on the tightest cluster of hits and claims to be a sharpshooter.”
This joke is also where the fallacy gets its name.
An example of this fallacy that can
be seen in society today in Trump's claim that: “When Mexico sends its
people, they’re not sending their best. They’re not sending you. They’re
sending people that have lots of problems, and they’re bringing those problems
with us. They’re bringing drugs. They’re bringing crime. They’re rapists. And some, I assume, are good people.” This
claim was established by looking at America’s rates and percentages of crime in
certain areas of the country, the populations of jails, and the war on drugs
that is always such a big issue in America. He ignored the statistics that would
have revealed the number of Mexicans that come here legally (and illegally) and contribute to
the economy, people that come here illegally but aren’t involved in selling drugs,
aren’t rapists, aren’t involved in any form of crime, don’t bring any problems, and
eventually do become legal citizens. He made this statement to appeal to his
followers that think illegal immigrants, and immigration in general, are the causes
of many of America’s problems. He also included the line “And some, I assume,
are good people” to protect himself a little from the backlash of making such a
broad original statement, but highlighted the pieces of data collected that
were in favor of his own views and got his main idea across: that we shouldn’t be
letting them into our country because of the risk of more crime.
Another example of this can be seen
in the film A2-B-C, a Japanese film that shows how Japanese children from the
Fukushima Prefecture have begun to show thyroid abnormalities since the 2011
nuclear disaster. In this film, they explain how the government discredits all
claims that these abnormalities are occurring to avoid persecution from their
citizens for handling the situation, and the nuclear disaster in general, so
poorly. The Japanese government discredits claims by finding groups of children,
out of many groups of children that were examined near areas of radiation, that
don’t have any signs of thyroid abnormalities. They then claim that because these
children don’t have anything wrong with them, nothing is wrong with the areas people
are living in that have been known to have high levels of radiation. They
ignore the cases of children that do have these thyroid cysts to avoid
persecution for not moving people out of these areas with high levels of radiation,
and to avoid the repercussions from society for letting this happen to children.
They even go so far as to skew their own data by sending doctors
into small villages, just so those doctors can say that they found nothing wrong with the children.
But, when the parents took those same children to different doctors, most of them
did in fact find the thyroid cysts that the previous doctors said weren’t there.
These examples show how easily it
is for people to pick out pieces of date that don’t necessarily represent the
big picture. They show how people use that data to come up with a false conclusion that fits
their narrative. When people don’t take into account the whole collective of
data, they aren’t being honest with their audience or with themselves. It can discredit a person if enough people
find out and call them out on it, and it can make people much less trusting of
what they hear from that person, or an organization in general.
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