Uncertainty

Uncertainty is a condition in which relevant outcomes, probabilities, or causal relationships are not fully known. It can arise from limited information, inherent variability, or imperfect models.

Probability: A mathematical measure of how likely an event is, ranging from zero to one. Uncertainty about outcomes is often expressed as probabilities.
Bayesian inference: A method for updating probabilities as evidence becomes available. It revises uncertainty by combining prior beliefs with observed evidence.
Risk: The possibility of harm or loss, often assessed by its likelihood and severity. Risk combines uncertain outcomes with their potential consequences.
Ambiguity: The presence of multiple plausible meanings, interpretations, or classifications. Ambiguity concerns unclear meanings, not merely unknown outcomes or probabilities.
Randomness: A property of processes whose outcomes cannot be predicted with certainty from available information. It distinguishes inherent unpredictability from uncertainty caused by missing knowledge.
Confidence interval: A range produced by a statistical procedure that covers a parameter at a specified rate over repeated samples. It expresses sampling uncertainty around an estimated quantity.
Weather forecasting: The use of observations and models to predict atmospheric conditions. Forecasts communicate uncertainty about future weather and its timing.
Aleatory uncertainty: Uncertainty attributed to inherent variability or randomness in a process. It contrasts with uncertainty caused by limited knowledge.
Ignorance: A lack of knowledge or awareness about a subject or fact. Uncertainty often reflects specific gaps in what is known.
Monte Carlo method: A computational method that uses repeated random sampling to estimate quantities or explore possible outcomes. Simulation propagates uncertain inputs through complex calculations.

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