False Precision (Spurious Accuracy)

Also Known As: Overprecision Misplaced Precision Spurious Precision

Statistical Error ID: false_precision

Definition

False Precision occurs when data is presented with more decimal places or significant figures than the underlying measurement justifies, creating an illusion of accuracy. This is one of the most common and least recognized statistical errors in media, business, and everyday communication. The extra digits imply a level of certainty that simply doesn't exist in the data.

Examples

A health study reports that people who sleep '7.43 hours' per night have optimal cognitive performance. The underlying data came from self-reported sleep estimates rounded to the nearest half hour. The extra decimal places convey a precision that the measurement never had.

A business case projects that the new product will generate €3,847,512 in revenue in year one. The forecast is built on market size estimates (±30%), conversion rate assumptions (highly uncertain), and pricing that hasn't been finalised. The apparent precision is mathematical artefact, not genuine accuracy.

Converting miles per gallon to litres per 100 km yields values like 7.84 L/100km from an original '30 mpg.' The source measurement was accurate to the nearest whole number; the conversion produces false sub-litre precision that implies a level of measurement that never existed.

Verification Steps

Verification Steps

Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.

Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

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Binary (yes/no) questions an LLM must answer to identify this aspect: