Almost never, and the gap between score and probability quietly breaks thresholds, expected-value decisions, and customer trust. What calibration is, how to measure it, and the two fixes that work.
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Your model says '90% probability.' The customer asks if that means it's right 90% of the time. Is it?
Almost never, and the gap between score and probability quietly breaks thresholds, expected-value decisions, and customer trust. What calibration is, how to measure it, and the two fixes that work.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
UP NEXT ON YOUR JOURNEY
Next in this trackExplain what a p-value actually is, to me, and then to a customer's VP of Marketing.Next in this trackDesign an A/B test for a customer's new recommendation model. How long do you run it, and on how many users?Next in this trackThe customer checked the A/B dashboard daily and stopped the test the day it hit significance. What's wrong, and what do you tell them?
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