📊 Data doesn’t lie — but stories can
Michal Szudejko makes it clear: data narratives can be so powerful that, intentionally or not, they become manipulation.
The article identifies 4 key biases that distort data-driven stories:
🎯 1. The data-interpretation gap — The analysis appears on page 3, the conclusion on page 23. The audience never connects the dots.
🍒 2. Cherry-picking — The extreme example: tobacco companies arguing in court “yes, cigarettes cause cancer… but not in the people suing us.”
🖼️ 3. Framing — “Unemployment drops to 4.9%” vs. “Millions still jobless despite slight drop.” Both are true. The difference is emotional.
📉 4. Misleading visualizations — Truncated axes, excessive colors, 3D charts, manipulated scales. 14 tactics that make a chart say the opposite of reality.
✅ The solution: present data in full context, show methodology, and clearly separate facts from interpretations.
Quick explanation#
Imagine a real estate agent shows you only the front photo of an apartment. Beautiful. But if you walk around back, there’s a public toilet right below the balcony. That’s misleading storytelling: not lying, but choosing what to show. The same thing happens with data.
More information at the link 👇
