![]() ![]() Science is humanity’s tool for better understanding the world. "Painting the target around the matching profile: the Texas sharpshooter fallacy in forensic DNA interpretation".The rise of Big Data, data science and predictive analytics to help solve real world problems is just an extension of science marching on. Spoiler alert: they tested for so many potential diseases they where bound to find a match in the data set. A good example of this fallacy can be found in a Swedish study looking into the potential health impact of living near power lines.Then just compliment them with their skills. And just to validate it, ask them to shoot again, but this time with a new target or using a new data set. Ask them what was there first, the hole or the target sheet. But the next time someone shows you a perfect bulls eye score, either on a target sheet or more likely on a business data set. If you want to know why you should be cautious in general with over confident people click here.And re test the hypothesis using a new data set. Ask wether the data was gathered before or after the hypothesis was made. Be aware if someone seems overly confident about the match between data and a hypothesis.This way you are sure you don't retro-fit. First build a hypothesis and only than gather data.Gathering a new data set with this hypothesis might easily proof you wrong. But you would have also committed the Texas Sharpshooter fallacy. If you use your existing data set (the 10 drunk lads you registered) you would be right, wouldn't you. Based on this data set, your hypothesis might be that all British tourist in this Amsterdam Hotel are drunk. And in your data set it shows that all 10 British tourist are drunk. Let's say you decided to do a test and count all the drunk tourist in your hotel lobby. It's not your fault the human brain is a great pattern recogniser. Because you think you see a pattern and then later use that same data set to proof there is a pattern. Where things go wrong is when you build a hypothesis once you have examined your data set. Since their findings will either proof of disproof the hypothesis. Usually we won't find this fallacy if people have a clear idea what they want to test before they start. So you start building a hypothesis after you examined it. This fallacy often occurs when you don't have a clear idea of what it is that you want to test before gathering the data. ![]() Just like the circles drawn by the Texas “sharpshooter.” When and where does it happen? ![]() What may look as a relevant cluster of data points might actually turn out to be totally meaningless. You can run into this fallacy in research or in data rich business environments. And they are all perfectly in the bull’s-eye! What a sharpshooter that is! Who is this man? The shooter later admits that he just shot at the barn and than drew the circles around the shots. It's a story about someone in Texas who sees bullet holes on the side of a barn. The fallacy was named after a Texas Shooter. Say what? What it comes down to is that you're making a point but your argument or data sucks. That's a form of fallacy that occurs when the content of an argument's premises fails to adequately support its conclusion. The Texas Sharpshooter fallacy is what we call an informal fallacy. This fallacy shows how we tend to ignore differences in data and stress similarities So what? ![]()
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