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In London’s Soho, there’s a quiet water pump with a loud story. In 1854, it stood at the center of a deadly cholera outbreak. But its importance wasn’t just about disease. It was about how people ignored data in favour of a story they believed. This pump is a powerful narrative bias example, showing how stories can mislead—even when the stakes are life and death.
John Snow vs. The “Miasma” Myth
London in 1854 was in chaos. Cholera swept through the city, killing thousands. People were desperate for answers, and the prevailing belief—the “miasma theory”—offered one: diseases spread through bad air.
Enter Dr. John Snow, a physician with an audacious idea. Snow believed cholera wasn’t airborne but waterborne. To prove it, he meticulously mapped cholera cases across Soho, pinpointing the Broad Street pump as the source. His evidence was groundbreaking, logical, and clear.
Yet, his findings were largely dismissed1John Snow failed to convince many in the medical establishment. (Science Museum)..
Why? Because Snow’s data didn’t fit the narrative everyone believed. The miasma theory wasn’t just a scientific explanation; it was a story people had trusted for centuries2The ancient Greek physician Galen, working in the 2nd century CE from the medical principles of Hippocrates and others, was the primary proponent of the idea of diseases caused by miasma (pollution) or poor quality air. (Science Direct).. It was simple, familiar, and comforting.
The logic was simple: cities stank, diseases spread in cities. So, the smell must be the culprit.
Snow’s evidence, in contrast, was unsettling. It asked people to rethink everything they “knew” about disease. And so, they resisted.
Eventually, Snow convinced authorities to remove the pump handle, stopping the outbreak. But the shift in public health thinking? That came much later.
The lesson here? Humans love stories—even when they’re wrong.
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What is Narrative Bias?
Narrative bias is our brain’s tendency to favour stories over data.
We crave cause-and-effect explanations, even when reality is messy and random.
Stories feel satisfying—they simplify complexity, connect events, and give us meaning.
But this mental shortcut often leads us astray, making us ignore evidence that doesn’t fit the narrative.
When Data Gets Lost in the Plot: Narrative Bias Examples
Here’s a quick thought experiment:
Who’s more likely to read The New York Times in the U.S.?
A) Someone with a Ph.D. 👨🏻🔬
B) High School Grad Without a College Degree 🏫
If you guessed “A”, you’re not alone, but you’re also wrong. While the NYT may seem like a lot of people with Ph.D. would read, there are only about 6.57 million Ph.D. holders in the U.S., compared to over 94 million NYT readers. Statistically, the odds favour “B”.
“Ph.D. holder reading the NYT” fits a tidy story.
Our love for stories over raw data is clear to authors from the sheer number of books on storytelling being sold on Amazon. Lead with a Story was one of the first books I picked up when I started working, and it completely changed the way I thought about communication in business.
Consider Malcolm Gladwell’s bestsellers like The Tipping Point or Outliers. They hook us with captivating anecdotes that frame complex ideas.
While the “Malcolm method” is brilliant storytelling3Read the tyranny of Malcolm’s., it sometimes oversimplifies reality or draws conclusions that don’t always hold up under scrutiny.
It works because it leans on narrative bias: we’re primed to believe a good story, even if the data behind it is shaky.
Here are some more narrative bias examples. It’s:
🤑 Why investors bet on a company’s story instead of digging into its income statement or balance sheet?
👟 Why Nike makes you believe greatness starts with three words: Just Do It?
😎 Why we love the idea that a college dropout built a tech empire with nothing but grit?
From Survival to Storytelling: How We Got Here
Humans are meaning-making machines.
Our brains are wired to find patterns and construct narratives from fragments of information. This evolutionary quirk helped our ancestors survive by spotting danger and predicting outcomes4Why Did Humans Evolve Pattern Recognition Abilities?.
But in modern life, it backfires. Here’s why:
Oversimplification: The Brain Loves Patterns
Stories often exclude messy, inconvenient details that don’t fit the narrative.
Take Mark Zuckerberg’s story: a college dropout who built a tech empire. It’s tempting to conclude that dropping out might be the secret to success. Our brains latch onto this success story because they’re easier to process.
Narrative bias fuels this oversimplification, often working hand-in-hand with survivorship bias. We focus on the visible successes (the survivors) while ignoring the many dropouts who didn’t make it.
It creates a tidy, but incomplete, narrative: “They succeeded because of X”.
Selective Memory: The Trickery
Memory isn’t a perfect recording; it’s a reconstruction. When recalling events, we fill in gaps to create a logical sequence, even if the actual events were chaotic or random.
For instance, if you believe a political party is corrupt, you’re likely to remember scandals that confirm your belief while ignoring evidence to the contrary, forming a coherent narrative in your memory.
This overlaps with confirmation bias, reinforcing our faulty narratives.
Comfort Over Complexity
A coherent story feels safe. Randomness, on the other hand, feels unsettling.
We would rather believe in a simple cause-and-effect explanation than accept that luck or chance might be at play.
As Nassim Taleb explains in The Black Swan, randomness is complex and impossible to reduce without losing meaning. In contrast, tidy stories can be compressed into neat summaries, which is why they’re far more comforting—even when they’re inaccurate.
Breaking Free from the Bias Trap
The good news? Recognising narrative bias is the first step to overcoming it. Here’s how you can break free:
⏸️ Pause Before You Believe: When someone shares a compelling story, ask yourself: does this feel true, or is it actually true? As Charlie Munger reminds us, look for disconfirming evidence.
🕵️♂️ Hunt for Survivorship Bias: If someone claims, “X did Y to succeed,” ask:
“How many others did Y but didn’t succeed?”
“What factors besides Y might explain their success?”
🤔 Ask “What’s Missing?”: Good stories omit details to stay engaging. Dig deeper to find the pieces that were left out.
The Truth Isn’t Always a Story—But It’s Always Powerful
John Snow’s work teaches us more than just the origins of modern epidemiology. It’s a cautionary tale about the seductive power of stories. Snow’s evidence took time to triumph because it disrupted the comforting narrative of his era.
Today, narrative bias still shapes how we interpret the world. It’s why we fall for simplistic success stories, believe in tidy political explanations, or blame single causes for complex problems.
But life isn’t a Hollywood script. The truth may not always fit neatly into a story, but it’s far more valuable than fiction.
Footnotes:
- 1John Snow failed to convince many in the medical establishment. (Science Museum).
- 2The ancient Greek physician Galen, working in the 2nd century CE from the medical principles of Hippocrates and others, was the primary proponent of the idea of diseases caused by miasma (pollution) or poor quality air. (Science Direct).
- 3Read the tyranny of Malcolm’s.
- 4
