Noise

The Hidden Twin of Bias

There is a shooting range in your head. Every judgment you make is a shot at a target, and every shot you fire lands somewhere. Sometimes you hit dead center. More often you miss, and the question that should haunt you, but rarely does, is how exactly you missed.

Picture four teams of marksmen, each firing five shots at the same target. Team A puts every shot near the bull's-eye. Team B clusters tightly, but well off to one side, hitting almost the same wrong spot five times in a row. Team C sprays its shots all over the target, the center of the spread close to the bull's-eye but no individual shot anywhere near it. Team D is the worst of both worlds: a wide scatter, far from the center.

Team B is biased. Something predictable is pulling its shots in one direction, perhaps a misaligned sight, perhaps an instructor who taught everyone the same wrong stance. Team C has a different problem entirely. Each shooter aims at the right place, but their hands shake, their eyes flicker, the wind moves and they don't compensate for it the same way twice. Team C is noisy.

This little metaphor opens "Noise," the book Daniel Kahneman, Olivier Sibony, and Cass Sunstein published in 2021, and it carries the whole argument. Bias is a systematic error: you can name it, point at it, and at least imagine fixing it. Noise is something stranger. Noise is unwanted variability in judgments that should have been the same. Two judges, same case, very different sentences. Two doctors, same patient, very different diagnoses. Same forecaster, same question on two different mornings, very different answers. Noise.