Uncertainty

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(Redirected from Scientific uncertainty)

Concept of uncertainty in various fields



Uncertainty refers to situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty arises in partially observable or stochastic environments, as well as due to ignorance, indeterminacy, or lack of knowledge.

Types of Uncertainty

Uncertainty can be classified into different types based on its nature and source:

Aleatory Uncertainty

Aleatory uncertainty, also known as statistical uncertainty, is due to inherent randomness or variability in a system. It is often modeled using probability distributions.

Epistemic Uncertainty

Epistemic uncertainty arises from a lack of knowledge or information about a system. It can often be reduced by gathering more data or improving the model.

Uncertainty in Science

In scientific research, uncertainty is a critical component of data analysis and interpretation. Scientists use statistical methods to quantify uncertainty and to make informed decisions based on incomplete information.

Measurement Uncertainty

Measurement uncertainty is the doubt that exists about the result of any measurement. It is expressed as a range within which the true value is asserted to lie with a certain level of confidence.

Uncertainty in Physics

In physics, the concept of uncertainty is famously encapsulated in Heisenberg's uncertainty principle, which states that certain pairs of physical properties, like position and momentum, cannot both be known to arbitrary precision.

Uncertainty in Economics

In economics, uncertainty is a key factor in decision-making processes. It affects consumer behavior, investment decisions, and policy-making.

Risk vs. Uncertainty

Economists distinguish between risk, which can be quantified and managed, and uncertainty, which is unquantifiable and unpredictable.

Uncertainty in Decision Making

Decision-making under uncertainty involves choosing actions based on incomplete information. Various models and frameworks, such as decision trees and Bayesian inference, are used to handle uncertainty in decision-making processes.

Related pages

Uncertainty can lead to multiple possible outcomes.
Graphical representation of uncertainty.
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Contributors: Prab R. Tumpati, MD