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

9.2K
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...
9.2K
Confidence Intervals01:21

Confidence Intervals

9.7K
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...
9.7K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.9K
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...
8.9K
Confidence Coefficient01:24

Confidence Coefficient

10.0K
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...
10.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.6K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.6K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.9K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Consciously detecting and recognizing a past visual word after its sensory trace is gone.

Communications psychology·2026
Same author

Effects of Early Adversity and War Trauma on Learning Under Uncertainty.

Developmental science·2025
Same author

Navigating the COVID-19 infodemic: the influence of metacognitive efficiency on health behaviours and policy attitudes.

Royal Society open science·2023
Same author

Facial emotion recognition in refugee children with a history of war trauma.

Emotion (Washington, D.C.)·2023
Same author

Perceptual decisions and oculomotor responses rely on temporally distinct streams of evidence.

Communications biology·2022
Same author

Sensory Development: Integration before Calibration.

Current biology : CB·2020
Same journal

An effort recalibration framework for digital media use and cognition.

Nature human behaviour·2026
Same journal

Interoception in self-harm and suicide: a scoping review and meta-analysis.

Nature human behaviour·2026
Same journal

Trusting the body and self-harm.

Nature human behaviour·2026
Same journal

Building capacity for unity in diversity.

Nature human behaviour·2026
Same journal

Secondhand smoke exposure and human health: an umbrella review.

Nature human behaviour·2026
Same journal

Genome-wide association studies of infant and toddler temperament in European and multi-ancestry populations.

Nature human behaviour·2026
See all related articles

Related Experiment Video

Updated: Dec 8, 2025

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.4K

Discrete confidence levels revealed by sequential decisions.

Matteo Lisi1, Gianluigi Mongillo2,3, Georgia Milne4

  • 1Department of Psychology, University of Essex, Colchester, UK. m.lisi@essex.ac.uk.

Nature Human Behaviour
|September 22, 2020
PubMed
Summary
This summary is machine-generated.

Humans express confidence in uncertain events, but not always with ideal Bayesian probability. A new dual-decision task shows confidence guides choices but relies on discrete levels, not full probability distributions.

More Related Videos

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

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

Published on: September 19, 2012

12.3K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.5K

Related Experiment Videos

Last Updated: Dec 8, 2025

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.4K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

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

Published on: September 19, 2012

12.3K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.5K

Area of Science:

  • Cognitive psychology
  • Decision science
  • Neuroscience

Background:

  • Humans express confidence in uncertain events, ideally aligning with Bayesian probabilities.
  • Previous research faced challenges in quantitatively comparing self-reported confidence with normative predictions.

Purpose of the Study:

  • To objectively measure human confidence in decision-making.
  • To assess whether human confidence aligns with normative Bayesian models or alternative strategies.

Main Methods:

  • Development of a dual-decision task where the first decision's accuracy dictates the second decision's correct answer.
  • Objective measurement of confidence through behavioral outcomes rather than self-reports.

Main Results:

  • Participants utilized confidence to enhance performance in the dual-decision task.
  • Human confidence levels were better described by a model with discrete confidence states than by ideal Bayesian probabilities.
  • Behavioral data indicated a deviation from normative Bayesian strategies.

Conclusions:

  • Human confidence judgments may not precisely follow normative Bayesian models.
  • Confidence might be based on simplified point estimates rather than comprehensive probability distributions.
  • Findings challenge the descriptive validity of normative accounts for human confidence.