Twyman's Law
Updated 2026-08-01
INTRODUCTION
English translation pending.
CORE DEFINITION
Coined by the media researcher Tony Twyman, the law holds that any data point which looks interesting or unusual is usually the product of a mistake. A result that seems too good to be true, or too strange to explain, is far more likely to come from a sampling error, a contaminated sample or a calculation slip than from a genuine discovery. The qualifier matters: this is not a claim that real anomalies never occur, but a default ordering of suspicion that puts debugging before interpretation.
SCAFFOLDING EFFECT
Reduce cognitive load
- Anomaly alarm: treat every surprisingly good result as a defect report before treating it as a discovery. - Debug first: rule out unit conversion, sampling and coding errors before you spend time interpreting. - Discovery filter: promote the anomaly to a finding only after an independent replication succeeds.
Anchor fast decisions
Data collection has far more ways to fail than to produce a genuine novelty, so the base rate of error is much higher than the base rate of discovery. A clean and striking pattern also raises the odds that a systematic artifact, such as a conversion or filtering error, is at work. The law therefore inverts the default: suspiciously tidy results should be presumed wrong until proven otherwise.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Twyman's_lawverified
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