Monday, February 29, 2016

Texas Sharpshooter Fallacy, Confirmation Bias and the search for the meaning of life

The Texas Sharpshooter Fallacy I believe, is the most fascinating out of the number of fallacies posted on the discussion suggestions. Mainly because of the story that came about it and why people might be committing the fallacy unknowingly. The fallacy is apparently named after a Texan used his gun to shoot the side of a barn and painted over where his bullet holes were; making him seem like he was a accurate shooter. I think this fallacy is interesting for the way it's using for reasoning. How people might use it to come up with reasons by accidentally using the fallacy.

The Texas Sharpshooter Fallacy is used when someone is attempting to find patterns within various data sets to set up their own argument or belief. For example, in a class I had last year someone let the teacher know it was going to be her golden birthday tomorrow and asked if she could bring in cupcakes for everyone. While having someone in a class of 15 have a golden birthday is highly unlikely in itself, there were two other people who had consecutive birthdays after her and were also golden birthdays. I know, right? Pretty crazy. Consecutive birthdays, golden birthdays, one on the 21st, one on the 22nd and one on the 23rd.

When our teacher learned about this, she freaked out and thought it was fate that we would be in the same class. She decided to go along with the girl who was brining in muffins and decided to bring in a cake for the next lecture. Surely, this must be some work of fate, that all these people were destined to be in the same class during the semester their birthdays would fall under. What's great about this fallacy is how people try to find patterns in things that have no connection. Like they are looking for the meaning of life, where everything happens for a reason. But, what ends up happening is that people who are using this fallacy it's because they are ignoring the rest of the data.

For example, each student could be taking the class because it's the last class they need for their major, minor or concentration. How many students have birthdays in that same month in the first place? Is it a popular month? I looked at an article on that talked about the theory. The writer wrote author David Ramsey and his book You Are Not So Smart where the author using the Lincoln-Kennedy conspiracy as an example of the fallacy.

Let's look at the facts. Both presidents were killed by firearms, the killers had 15 letters in their entire name, of which contained three. Neither would make it to trail. Both of their vice-presidents had the same last name. Then there's other small coincidences like Lincoln was killed in Ford Theater and Kennedy was killed in a Lincoln car that was made by Ford. It goes on and on. Surely there must be an answer as to why there are so many similarities. There must be a reason for everything. Sure, sometimes they can be but sometimes it's just a coincidence.

Let's look at the other facts: Lincoln was shot in Washington D.C. while Kennedy was shot in Texas. One was shot in a theatre while the other in a car. Lincoln was 6'4 and Kennedy was 6'0. Two of three people with birthdays was a girl and one was a guy. But looking for patterns in a data set isn't always bad. Let's look at examples of how this could or couldn't work. First, You go outside and see leaves that have fallen out of the tree and they've sort of formed a pattern that looks to be a Z. Second, you notice that 18th and O has had 4 car accidents in the last two weeks.

Which data set would be used to support a theory or argument? Obviously, the leaves, while interesting holds no value. It could be the wind, it could be the size of the leaves. There are a lot of variables. But 4 car accidents on one street? In the city of Lincoln where nothing ever really happens? This could be used in an argument as long as the facts back it up. Maybe there is a giant pothole where each car drove into. Maybe there's something else wrong with the street or street light. So all in all while it's easy to look at the similar patterns but it's also as easy to look at the dissimilar patterns.

1 comment:

  1. The Texas Sharpshooter fallacy appeals to me the most because analyzing data is something that I find extremely fascinating. A documentary that opened my eyes to the importance of data analytics is the movie Freakanomics. By closely studying data, a pair of a mathematicians debunk common myths just by looking at numbers the right way. They prove the reason for the massive drop in crime in New York City, commonly credited to Mayor Rudy Giuliani getting tough on crime, was actually because of the passing of Roe v. Wade. Parents expecting unwanted children were simply not giving birth, hence resulting in a lack of criminals 20-30 years later. That is just one of many examples of how analyzing data in the right aspect can prove some unexpected results.
    However, after reading about the Texas Sharpshooter fallacy it is a little scary to wonder just many misleading statistics there are out there. An example common misleading statistic, one that I vividly remember hearing as a kid, is the 'myth' that power lines cause cancer in children. A study done in Sweden surveyed everyone living within 300 meters of power lines over a 25 year period. "The study found that the incidence of childhood leukemia was four times higher among those that lived closest to the power lines, and it spurred calls to action by the Swedish government. The problem with the conclusion, however, was that the number of potential ailments, i.e. over 800, was so large that it created a high probability that at least one ailment would exhibit the appearance of a statistically significant difference by chance alone."
    So that example of data being analyzed in incorrect lights is a little concerning. It makes me wonder just how many misleading conclusions there are out there, including all of the conclusions presented in the documentary Freakanomics.