Twyman's law¶
Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another".
Core Idea¶
Twyman's law is treated here as the recurring cross-domain formal modeling identity summarized by this source-grounded definition: Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another". Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result.
Scope of Application¶
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Documented setting. It is named after the media and market researcher Tony Twyman and has been described as one of the most important laws of data analysis.
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Documented setting. The law is based on the fact that errors in data measurement and analysis can lead to observed quantities that are wildly different from typical values.
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Documented setting. These errors are usually more common than real changes of similar magnitude in the underlying process being measured.
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Documented setting. For example, if an analyst at a software company notices that the number of users has doubled overnight, the most likely explanation is a bug in logging, rather than a true.
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Documented setting. The law can also be extended to situations where the underlying data is influenced by unexpected factors that differ from what was intended to be measured.
Clarity¶
A clear use of Twyman's law names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or.
Manages Complexity¶
Twyman's law compresses multiple cross-domain formal modeling details into a stable diagnostic relation. The source shows both the central mechanism—the law can also be extended to situations where the underlying data is influenced by unexpected factors that differ from what was intended to be measured.—and the practical consequence—for example, if an analyst at a software company notices that the number of users has doubled overnight, the most.
Abstract Reasoning¶
- Type the carrier. Identify the cross-domain formal modeling entities to which the claim applies.
- State the relation. Use the source-grounded identity: Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another".
- Check operation and conditions.
Knowledge Transfer¶
Within the home domain. Knowledge about Twyman's law transfers literally when a new case preserves the same carrier type, relation, and recognition test. It is named after the media and market researcher Tony Twyman and has been described as one of the most important laws of data analysis. The law is based on the fact that errors in data measurement and analysis can lead to observed quantities that are wildly different from typical values. Beyond the home domain. No canonical parent is asserted for Twyman's law.
Neighborhood in Abstraction Space¶
Twyman's law sits in a moderately populated region (48th percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.
Family — Inferential Fallacies & Research Biases (18 abstractions)
Nearest neighbors
- Data element — 0.87
- Control chart — 0.87
- Value at risk — 0.86
- Single Vegetative Obstruction Model — 0.86
- Automatic item generation — 0.86
Computed from structural-signature embeddings · 2026-10-08