Your culture taught you to draw a succession of imaginary lines to link the stars and form figures when, in reality, their position is determined by chance. In other words, you manipulate the data to confirm your hypothesis.Īnother example of this fallacy is the interpretation of star constellations. This fallacy would indicate you had a premonitory dream as 3 + 6 – 2 = 7 and this was the number you dreamed of. Imagine that you dream of the number seven while you stayed in hotel room number 362 (of which you were previously unaware of). To continue understanding the Texas sharpshooter fallacy, here’s an example you could easily find in your daily life. Examples of the Texas sharpshooter fallacy Thus, according to this fallacy, someone modifies observable data in order to confirm their hypotheses (just like the shooter in the story did). In other words, he altered the data (painted the targets) to confirm his hypothesis (he was a sharpshooter). Later, he painted a target centered on each one of them and proclaims himself a sharpshooter.”Īs you can see, the shooter took the necessary measures to make his action seem logical in order to prove his worth. Do more research.ĭata-based decision making can be a tremendous gift for your business, but only if you let the data tell the story for you, not the other way around.“There was a shooter who randomly fired several shots at a barn. Stay away from averages if you can avoid itĮvaluate the data against your hypothesis and against your anti-hypothesisĪbsence of evidence is not evidence of absence. It relies on the inputter as well as a set of variables specific to your business.ĭata is a tool. You allow for variances in your data because no data is perfect. You are looking for what statisticians refer to as a 95% confidence level which means that 9.5 times out of ten this result will be the same. If the answer to both questions above is yes you’ve fallen victim to the sharpshooter fallacy.Ĭonclusions from data should be exact and specific. Next, use the same dataset to verify the opposite of your hunch. Next, compile a sample data set based on your hunch. You know it without the benefit of being able to explain it. The bigger your dataset gets, the harder it is to identify where the simple insights are gathered.ĭrawing conclusions from your data always starts with a hunch. A grandparent holding their infant octuplet grandchildren will have an average age of 18. Place your feet in the desert and your head in the arctic, your mean temperature is 75. The danger comes from three things:ġ) Finding data to fit your pre-conceived conclusions (cherry picking)Ģ) Assuming that because you can’t find it, it doesn’t existģ) Averages or targeting the “mean”. The challenge always comes from the interpretation of the data. It gives a person a clear, objective picture of their reality. It illustrates how people look for similarities, ignoring differences, and do not account for randomness.ĭata is a wonderful thing. The Texas Sharpshooter Fallacy is a logical fallacy based on the metaphor of a gunman shooting the side of a barn, then drawing targets around the bullet hole clusters to make it look like he hit the target. When reviewing your data, don’t fall victim to the Texas Sharpshooter Fallacy. The benefit or danger of a tool comes from the person wielding it. Like a shovel, it can dig a hole or it can kill zombies. The Manager says to the accountant, “What’s 2+2?” The accountant replies, “what do you want it to be?”ĭata is a tool.
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