How the Texas Sharpshooter Fallacy Skews Sports, Tech & Everyday Decisions

In statistics there is an interesting concept called the Texas Sharpshooter Fallacy (full name: Texas Sharpshooter Fallacy). It describes a story where a Texan shoots at a barn wall, then draws a target around a cluster of bullet holes to create the illusion of a sharpshooter.

A Texan fires many shots at a barn wall, then draws a target around the densest cluster of bullet holes, creating the false impression of a skilled marksman.

The story also has a Chinese version called “shoot the arrow first, then draw the target,” which is a causal fallacy.

Simply put, it means deliberately selecting data or evidence that supports one’s viewpoint from a large pool while discarding the rest that contradicts it.

Although amusing, the fallacy is quite “practical”; it appears in sports competition, lottery prediction, and even technology innovation, influencing public perception.

Texas Sharpshooter Fallacy in Sports

Sometimes an athlete’s or team’s “highlight moments” are not representative of normal performance but are the result of selective data presentation. For example, a player may have many impressive clips—fast breaks, precise three‑pointers, spectacular blocks—making it seem as if they dominate the entire game.

But why did they still lose?

The real data might look like this:

Overall shooting percentage is low , yet the video only shows made shots.

Turnover count is also high , but those moments are omitted from the highlights.

This is the “shoot the arrow first, then draw the target” effect: the audience sees only the curated exciting moments and ignores the full reality of the match.

Texas Sharpshooter Fallacy in Tech Companies

Tech firms and research institutions sometimes use this fallacy to package success stories. For instance, some AI companies claim their algorithms outperform humans on certain tasks, but they do not disclose that they repeatedly tweaked the dataset across many failed experiments until they achieved the desired result.

Start‑ups may only promote their most successful products while ignoring numerous failed projects, giving investors the illusion that “their innovation always succeeds.”

Medical research may publish only effective experimental data while withholding failed trials, leading to the mistaken belief that a drug has “excellent efficacy.”

This phenomenon is common; if you are not careful, manipulated data can directly lead to manipulated perception.

How to Avoid the Texas Sharpshooter Fallacy?

Since the fallacy is pervasive, here are several simple ways to avoid its trap:

Ask where the data comes from.

Consider whether other data might be hidden.

Check if there are failed cases that were not mentioned.

For example, when you see a startup’s success story, ask yourself whether it has other failed products and whether competitors have similar success cases.

Also, try to find more complete data rather than focusing on a cherry‑picked subset. If a research result sounds extraordinary, examine the original data: does it have a sufficient sample size, was it randomly selected, and are the statistical methods sound?

Additionally, consult multiple sources and look for independent studies supporting the conclusion. If a result appears only in a company’s report and no other institution has verified it, treat it with skepticism. Often we only see the “good news” that has been filtered out; if several independent bodies reach similar conclusions, credibility increases.

The most important thing is to maintain rational thinking and not be swept away by data that looks too good. We all like hearing promising news—new technology that will change the world, a financial strategy that promises quick wealth, or a health supplement that improves wellbeing. But if we find ourselves too eager to believe something, we should pause and ask: “Is this really true, or is it just ‘shoot the arrow first, then draw the target’?”

In short, the essence of the Texas Sharpshooter Fallacy is using selective data to mislead, and the best way to avoid it is to learn to look beyond the surface numbers and seek the truth hidden in the data. (Author: Wang Haihua)

For further reading, a recommended book on data‑analysis thinking is Data Analysis Thinking: A General Course , which offers easy‑to‑understand examples that can help you avoid many cognitive “pits.”