Hey, what’s up! So, before we dive into today’s fallacy, I just want to pause and say “wow!” y’all, I am blown away by the interest that we’ve had in this podcast! At the time of this recording, the podcast has only been out two weeks, and we’re already over 6,000 downloads.
That’s amazing to me! I’m so ridiculously thankful that this seems to have struck a nerve, and I’m so thankful that you’re here! Thank you for the feedback you’ve given me, for sharing with your families, students, communities and especially with the young people in your life - that’s who I’m most excited to be sharing this message of critical thinking with! Hopefully we can get more people asking good questions & spotting bad reasoning and actually thinking about the things they’re hearing rather than just believing everything they hear!
Ok, let’s dive into today’s new Fallacy: The Texas Sharpshooter. The Texas Sharpshooter fallacy is committed when similarities in data are over-emphasized, but differences in same data are ignored. This fallacy is based on our human tendency to look for patterns and similarities while dismissing differences and ignoring randomness.
As I was researching this fallacy, I discovered it comes from the story of a fictitious man in Texas who was not a very good shot. He went out back and fired a bunch of shots into the side of a barn, then walked up to it, found the largest cluster of bullets and then painted the bullseye on top of that cluster of bullets….making it appear that he was a really good shot and he proudly declared himself a sharpshooter!
Now, does it really mean he’s a great shot just b/c there happened to be a cluster of bullet holes all together? Should we assume that he’s a true sharpshooter? No! It would be ridiculous! He had crazy, random shots all over the side of that barn, so cherry-picking that small cluster of holes to represent his skill is completely inaccurate. However, for someone who came along later and didn’t know the backstory, and happened to see the cluster of holes with the target painted over it…well, they could be fooled. But the shooter really knows that he wasn’t actually aiming at that target, so the findings are just random.
Here’s a simple example of what this fallacy sounds like: “The last two times I walked into the living room, all 20 cats were sleeping on the couch…clearly, cats love sleeping on couches more than on beds!” Really? Is it really true that seeing the cats on the couch twice means they love couches more than beds? How about all the other days when they were sleeping on the beds? Or on the floors? Or on the windowsills or heating vents?
And by the way…no, we do not own 20 cats, lol…or any cats for that matter. We have a ridiculous goldendoodle named Ted.
But let’s talk about what’s wrong with the thinking here: The problem behind the Texas Sharpshooter fallacy is that the person either has a preconceived assumption and is just looking for something - anything - to try to prove their point, so they completely disregard *all* the data that’s available and ignore the possibility that what they’re seeing could just be random and so they just pick a small part of the data that seems to support their preconceived assumption. OR, the person could have no preconceived assumption, and just happen to see some random cluster of “data” and make an assumption from that random cluster.
Anywhere people are looking for meaning, you’ll find the Texas Sharpshooter fallacy in play.
This is a fallacy of collecting and interpreting data. And this is why in science & statistics, there are very specific rules to how you have to conduct experiments or surveys, and for how you collect & interpret the data…it’s so you don’t commit a fallacy like this one!
Here’s another example of how people sometimes do this. Let’s say you go out on a first date with someone you’ve recently met, but don’t know really well yet. After chatting a while, you discover that you both have a dog named Ted…How funny! What a random thing to have in common! You chat some more and discover that both of your favorite books are To Kill a Mockingbird! Amazing! What are the chances! You have so much in common! The evening goes on and you discover that both of your families have vacationed at Myrtle Beach every summer and ate ice cream at the exact same ice cream shop.
Now, you have goosebumps. It seems like it’s getting almost creepy how much you have in common, and you start to think…”there’s no way we could have this much in common just by chance. Maybe this means something…maybe it’s just “meant to be” that we should meet & have a relationship!” and the next thing you know, you’re thinking all these things are “signs” or “proof” that you’re meant to be together. Now hold on there, Sharpshooter!! Let’s look at the bigger picture here: Think about it…Teddy Bear is a pretty common name for dogs, and it’s not unusual to shorten it to Ted when you’re yelling at the dog for being naughty (like we do on the regular)...and how many people read To Kill a Mockingbird in high school? Lots! And if you both live on the East Coast, it’s very likely that you visited Myrtle Beach in the summer - tens of thousands of people do it every year! Yet, here you are, basing this huge decision off this one small cluster of bullet holes in the proverbial barn, where if you stepped back and looked at the bigger picture, you might see 20 other “bullet holes'' of things you discussed that were nowhere near each other.
The Texas Sharpshooter fallacy caused you to ignore all those other areas where you had nothing in common and focus in on these few that were similar.
So, here’s the question to ask yourself if you think you’re facing a Texas Sharpshooter Fallacy: “Is it really true that these similarities indicate a pattern or really mean something specific or could this just be due to random chance? … *repeat*
Join me in the next episode when we’ll be discussing: Circular Reasoning
Remember: When you learn HOW to think, you will no longer fall prey to those who are trying to tell you what THEY want you to think and it all starts with asking one simple question: “Is that really true?”
