What It Is
Cherry-picking occurs when reporters or outlets select only the data, examples, statistics, or evidence that support a particular narrative while ignoring equally valid information that contradicts it. The result is a misleading picture that appears data-driven.
How It Works
With any complex topic, data points can often be found to support multiple conclusions. Cherry-picking involves selecting a starting point, time frame, comparison, or subset that produces the desired result—while that selection itself isn’t highlighted.
Real-World Example
Reporting on crime trends:
- Cherry-picked for decline narrative: “Violent crime has dropped 15% since 2020” (choosing a pandemic-spike baseline)
- Cherry-picked for increase narrative: “Violent crime is up 30% compared to 2019” (choosing a pre-pandemic low point)
- Complete picture: “Violent crime spiked in 2020, has since declined, and remains 12% above 2019 levels”
Both cherry-picked versions use real numbers but create opposite impressions by selecting different baselines.
How to Spot It
- Check the time frame - Why did they start measuring from this particular date?
- Look for missing comparisons - What other relevant data points exist?
- Question the sample - Is this representative or a selected subset?
- Seek context - How does this data point fit the larger trend?
- Ask who benefits - Does this selection favor particular interests?
Why It Matters
Cherry-picking is especially dangerous because it uses real data. Readers trust numbers and statistics, making cherry-picked data more persuasive than outright false claims. It creates false impressions while maintaining technical accuracy.
Related Bias Types
- Omission Bias - Leaving out important facts
- Bias in Numbers - Misleading use of statistics
- Confirmation Bias - Seeking confirming information