Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

12.1K
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...
12.1K
Confidence Coefficient01:24

Confidence Coefficient

10.9K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.9K
Confidence Intervals01:21

Confidence Intervals

11.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
11.1K
Uncertainty: Overview00:59

Uncertainty: Overview

1.9K
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.
1.9K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.4K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.4K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.1K
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...
2.1K

You might also read

Related Articles

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

Sort by
Same author

A disinhibitory basal forebrain-to-cortex projection supports sustained attention.

Cell·2026
Same author

Distributed control circuits across a brain-and-cord connectome.

Nature·2026
Same author

Parallel processing chains span cytoarchitectures to organize association cortex.

bioRxiv : the preprint server for biology·2026
Same author

A cortical circuit for orchestrating oromanual food manipulation.

Neuron·2026
Same author

A catecholamine-independent pathway controlling adaptive adipocyte lipolysis.

Nature metabolism·2026
Same author

Exploiting correlations across trials and behavioral sessions to improve neural decoding.

Neuron·2025
Same journal

Neural timescales from a computational perspective.

Nature neuroscience·2026
Same journal

Author Correction: Spinal cord Tau pathology induces tactile deficits and cognitive impairment in Alzheimer's disease via dysregulation of CCK neurons.

Nature neuroscience·2026
Same journal

Hippocampal theta sweeps indicate goal direction during navigation.

Nature neuroscience·2026
Same journal

Just how goal-directed are hippocampal theta sweeps, anyway?

Nature neuroscience·2026
Same journal

Goal-directed hippocampal theta sweeps during memory-guided navigation.

Nature neuroscience·2026
Same journal

Connectomic evidence that ordered activity drives neuromuscular network formation.

Nature neuroscience·2026
See all related articles

Related Experiment Video

Updated: Mar 25, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

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

Published on: September 12, 2014

15.9K

Confidence and certainty: distinct probabilistic quantities for different goals.

Alexandre Pouget1,2,3, Jan Drugowitsch1, Adam Kepecs4

  • 1Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland.

Nature Neuroscience
|February 25, 2016
PubMed
Summary
This summary is machine-generated.

The brain uses probabilistic computations for decision-making under uncertainty. This study distinguishes confidence (probability of decision correctness) from certainty (encoding of other probability distributions) for better understanding brain function.

More Related Videos

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Related Experiment Videos

Last Updated: Mar 25, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

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

Published on: September 12, 2014

15.9K
Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Adaptive behaviors under uncertainty rely on brain's probabilistic computations.
  • Certainty and confidence are often conflated due to their probabilistic nature.

Purpose of the Study:

  • To computationally distinguish between confidence and certainty.
  • To propose precise definitions for confidence and certainty in decision-making.
  • To guide research on neural codes for these concepts.

Main Methods:

  • Theoretical analysis of probabilistic frameworks for decision-making.
  • Conceptual distinction between confidence and certainty.
  • Discussion of neural coding strategies.

Main Results:

  • Confidence is defined as the probability of a decision being correct given the evidence.
  • Certainty is defined as the encoding of all other probability distributions.
  • Distinguishing these concepts is crucial for complex sequential decisions.

Conclusions:

  • Clear definitions of confidence and certainty are essential for neuroscience.
  • Understanding neural codes for confidence and certainty aids in deciphering cortical contributions to decision-making.