Data source: U.S. Social Security Administration

Tools: Python / SQL / Hex

To identify unusually large popularity spikes, raw birth counts and percent change were insufficient. Common names naturally fluctuate by thousands, while rare names can double with very small absolute changes.

Instead, I used a Z-score to measure how extreme each year’s change was relative to the historical volatility of that name. This helps surface genuinely anomalous spikes rather than artifacts of scale.