Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Probability Laws

Probability Laws

Probability in Statistics

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reply to 'Boundary issues for multidimensional frameworks of representation'.

Nature reviews. Neuroscience·2026

Same author

Distilling noise characteristics and prior expectations in multisensory causal inference.

PLoS computational biology·2026

Same author

A neural mechanism for online discovery of latent contexts.

bioRxiv : the preprint server for biology·2026

Same author

Clarifying the conceptual dimensions of representation in neuroscience.

Nature reviews. Neuroscience·2026

Same author

Working Memory of Multi-Object Scenes in Primate Frontal Cortex.

bioRxiv : the preprint server for biology·2026

Same author

Circuit explained: How does a transformer perform compositional generalization.

PloS one·2026

Same journal

Body-Brain Integration: The Lower Brainstem in Sleep-Wake Regulation.

Annual review of neuroscience·2026

Same journal

Planning in the Brain: It's Not What You Think It Is.

Annual review of neuroscience·2026

Same journal

The Emerging Neurobiology of Psychedelics: Critical Periods, Metaplasticity, and Extracellular Matrix Remodeling.

Annual review of neuroscience·2026

Same journal

Rethinking Predictive Processing.

Annual review of neuroscience·2026

Same journal

Path Integration in Alzheimer's Disease: Orientation, Movement, and Theta Rhythmicity.

Annual review of neuroscience·2026

Same journal

The Cellular and Circuit Basis of Temperature Sensation in <i>Drosophila</i>.

Annual review of neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 26, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

Neural coding of uncertainty and probability.

Wei Ji Ma¹, Mehrdad Jazayeri

¹Center for Neural Science and Department of Psychology, New York University, New York, New York 10003;

Annual Review of Neuroscience

|July 18, 2014

Summary

This summary is machine-generated.

Organisms navigate uncertainty by representing it, leading to better decision-making. This study explores how the brain uses uncertainty, confidence, and probability knowledge.

Keywords:

Bayesian inference decision making perception population encoding

More Related Videos

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Apr 26, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Neuroscience
Decision Theory
Cognitive Science

Background:

Organisms constantly face uncertainty from sensory, motor, and reward processing.
Accurate decision-making often requires representing task-relevant variable uncertainty.

Purpose of the Study:

To formalize the problem of decision-making under uncertainty using Bayesian decision theory.
To review behavioral and neural evidence for the brain's use of uncertainty representations.

Main Methods:

Bayesian decision theory framework.
Review of existing behavioral studies.
Review of neuroscientific findings.

Main Results:

Formalization of decision-making under uncertainty.
Evidence suggests the brain explicitly represents uncertainty.
Confidence and probability knowledge are integral to this process.

Conclusions:

The brain likely utilizes explicit representations of uncertainty for improved decision-making.
Understanding these mechanisms offers insights into neural computation and behavior.