DataTerminology.comNEW
A measure of doubt about data, parameters, or model predictions.
DESCRIPTION
Uncertainty captures the lack of exact knowledge in measurements, data, parameters, or predictions. It arises from randomness (aleatoric) and incomplete knowledge (epistemic). Quantifying uncertainty informs risk assessment, decision-making, and model reliability. Confidence intervals, posterior distributions, and error bars communicate uncertainty. Standards like GUM guide measurement uncertainty reporting in science and engineering.
ALTERNATIVE DEFINITIONS
BIPM JCGM VIM
Uncertainty of measurement is a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand.
Formal metrology definition emphasizing dispersion of plausible values.
Wikipedia
Uncertainty refers to epistemic situations involving imperfect or unknown information.
Broad definition focusing on epistemic contexts beyond measurement.
NIST Engineering Statistics Handbook
Uncertainty quantifies the range within which the true value of a measured quantity lies, considering both random and systematic effects.
Highlights random and systematic contributions to uncertainty.
RELATED TERMS
Variance Confidence Interval Measurement Error Risk Uncertainty Quantification Bayesian Inference Aleatoric Uncertainty Epistemic Uncertainty Standard Deviation Error Bars
COMPETING TERMS
Certainty
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Analyst
EXTERNAL SOURCES
- Wikipedia - Uncertainty
- BIPM JCGM VIM - Official vocabulary of metrology
- NIST - Handbook sections on uncertainty
- NASA - FAQ on measurement uncertainty
TOP QUESTIONS
What is Uncertainty in data and models?
Uncertainty quantifies how much trust we place in data or predictions. It arises from randomness and incomplete knowledge.
Category: Overview
What are aleatoric and epistemic Uncertainty?
Aleatoric uncertainty is irreducible randomness; epistemic uncertainty comes from limited knowledge and can be reduced with more data or better models.
Category: Example
Why is Uncertainty important for AI?
It informs risk-aware decisions, enables calibrated probabilities, and supports safeguards in safety-critical applications.
Category: Relevancy
How do we measure Uncertainty?
Use confidence intervals, posterior distributions, prediction intervals, and variance estimates. In ML, apply calibration, ensembles, or Bayesian methods.
Category: Requirements
What mistakes occur when ignoring Uncertainty?
Overconfident decisions, misinterpretation of significance, and brittle models. Reporting only point estimates misleads stakeholders.
Category: Mistakes
What’s the risk of miscommunicating Uncertainty?
Stakeholders may overtrust predictions or misunderstand ranges, leading to costly errors and policy missteps.
Category: Risks
How should teams present Uncertainty to non-experts?
Use clear intervals, error bars, and calibrated probabilities with plain-language explanations and visual cues.
Category: Management
What’s a hot topic around Uncertainty?
Trustworthy AI emphasizes uncertainty-aware outputs, calibration, and governance to mitigate harm.
Category: Topic
Where do standards define Uncertainty?
Metrology standards like GUM and VIM formalize definitions and reporting approaches across scientific domains.
Category: Regulation