Spurious Correlation Detected!
As the number of pirates decreased, global temperatures increased. But this doesn't mean pirates were preventing climate change!
🏴☠️ Pirates vs. Climate Change 🌍
A classic example of spurious correlation to teach about statistical bias, confirmation bias, and why correlation doesn't imply causation.
The Spurious Correlation
This famous example shows how two completely unrelated variables can appear to be connected when plotted over time.
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Interactive Data Visualization
Pirates (count)
Global temp (°C)
Try the controls above to see how different visualizations can change our perception of the relationship between variables.
Understanding Bias
Why This Matters
This example demonstrates several types of bias that can lead to incorrect conclusions in data analysis and research.
🎯 Confirmation Bias
We tend to notice patterns that confirm our existing beliefs and ignore those that don't.
📈 Selection Bias
Choosing specific time periods or variables that support a desired conclusion.
🔗 Correlation vs Causation
Assuming that because two things happen together, one causes the other.
👁️ Pattern Recognition
Our brains are wired to find patterns, even when they don't exist.
💡 Classroom Discussion Prompts
- Hidden Variables: What other factors might explain both the decline in pirates and the rise in global temperatures?
- Time Period: Why might the chosen time period (1800-2020) be misleading?
- Data Quality: How reliable are historical pirate counts and temperature measurements?
- Reverse Causation: Could climate change have caused the decline in pirates?
- Third Variable: What might be a "third variable" that affects both?
- Testing Causality: How would you design an experiment to test if pirates actually affect climate?