The original title is: "The Mean Is Lying to You: Benchmarks Hide the Variance That Breaks Prod"

Short summary

Benchmark scores measure central tendency across fixed test sets, but production reliability depends on tail-behavior under continuously shifting real inputs. Two models with identical benchmark scores can have completely different failure profiles—averaging obscures these critical differences. Fix this by reporting percentile distributions, evaluating on product-specific slices, tracking consistency, and treating leaderboard improvements as hypotheses requiring validation against live traffic.

• •Benchmarks measure averages on static test sets, not reliability on real production traffic
• •Two models with identical benchmark scores can have completely different failure profiles—one random, one systematic
• •Track percentile distributions, consistency, and product-specific slices instead of relying on leaderboard deltas

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