A Collective Autopsy of Chart Crimes

Bar charts with truncated axes. Correlations that vanish when you break the data down. Trend lines that hide more than they reveal. Most misleading charts aren't made with bad intent. They come from well-meaning people making common mistakes. But intent doesn't change impact.

Charts are persuasive precisely because they appear rigorous. When data is visualised, we assume someone has done the work and checked the numbers. That assumption makes misleading visualisations dangerous, especially in public health, climate reporting, and policy debates where most readers won't scrutinise every plot they encounter.

This session takes the contrarian view. While much of the conversation in data visualisation focuses on how to make better charts, this dialogue asks the inverse: what makes charts fail? What are the systematic ways visualisations mislead, and how do we spot them?

We'll build a shared vocabulary around patterns like Simpson's paradox, base rate neglect, ecological fallacy, survivorship bias, and cherry-picking. For each concept, you'll see what it looks like visually, why it happens, and what cues to look for. Then the room works together, examining real visualisations from news, research, and reports to put that vocabulary into practice.

You'll leave not as a cynic, but as an informed sceptic. Sharper questions. Better pattern recognition. And a clearer sense of the pitfalls worth watching for in your own work.