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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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.
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Confidence Intervals01:21

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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.
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Confidence Coefficient01:24

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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...
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Uncertainty: Confidence Intervals00:54

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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...
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Confidence reflects a noisy decision reliability estimate.

Zoe M Boundy-Singer1, Corey M Ziemba1, Robbe L T Goris2

  • 1Center for Perceptual Systems, The University of Texas at Austin, Austin, TX, USA.

Nature Human Behaviour
|November 7, 2022
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Summary
This summary is machine-generated.

Human confidence in decisions isn't always accurate. A new model explains confidence by considering uncertainty about uncertainty, or meta-uncertainty, in decision-making.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Decision Science

Background:

  • Confidence judgments in decision-making are common but imperfectly reflect accuracy.
  • Factors like response biases and misjudged difficulty influence confidence reports.
  • Understanding the basis of confidence requires isolating its accuracy-tracking component.

Purpose of the Study:

  • To develop and validate a computational model of decision-making that explains choice-confidence data.
  • To investigate the role of 'meta-uncertainty' in modulating confidence judgments.
  • To determine if the proposed model accurately accounts for confidence across diverse tasks.

Main Methods:

  • Developed a computational model where confidence reflects an estimate of decision reliability.
  • Incorporated 'meta-uncertainty' (uncertainty about uncertainty) as a limiting factor in confidence estimation.
  • Tested the model's fit to choice-confidence data from six prior studies across perceptual and cognitive tasks.

Main Results:

  • The model accurately accounts for choice-confidence data across various tasks.
  • Meta-uncertainty was found to vary between subjects, be stable over time, and generalize across domains.
  • Meta-uncertainty could be experimentally manipulated, further supporting its role.

Conclusions:

  • Confidence judgments are constrained by an individual's meta-uncertainty.
  • The developed model provides a parsimonious explanation for the computational basis of confidence.
  • This framework advances our understanding of decision-making and subjective confidence.