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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

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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.
A...
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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.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Coefficient01:24

Confidence Coefficient

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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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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Related Experiment Video

Updated: Jan 19, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
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Spatial confidence sets for raw effect size images.

Alexander Bowring1, Fabian Telschow2, Armin Schwartzman3

  • 1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.

Neuroimage
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces spatial Confidence Sets (CSs) to improve functional magnetic resonance imaging (fMRI) analysis by providing reliable effect size localization. The new method offers confidence in cluster size and location, addressing limitations of traditional mass-univariate approaches.

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Area of Science:

  • Neuroimaging
  • Statistical Analysis
  • Brain Imaging

Background:

  • Mass-univariate analysis is standard for fMRI but has limitations in localizing effects and providing confidence in cluster inference.
  • Current methods struggle with high statistical power leading to widespread null hypothesis rejection and lack confidence intervals for cluster size and location.

Purpose of the Study:

  • To address limitations in fMRI analysis by developing spatial Confidence Sets (CSs) for effect size maps.
  • To provide a method that offers confidence in the size and location of brain activation clusters beyond simple statistical significance.

Main Methods:

  • Extended a previously proposed method (Sommerfeld et al., 2018) to create spatial Confidence Sets (CSs) for thresholded raw effect size maps.
  • Applied the method to blood-oxygen-level-dependent (BOLD) fMRI contrast maps for percentage BOLD change inference.
  • Introduced theoretical and practical advancements to the original method, validated with 3D Monte Carlo simulations.

Main Results:

  • The developed CSs provide statements on locations where raw effect sizes exceed or fall short of a non-zero threshold, offering both upper and lower bounds.
  • The enhanced method demonstrates superior performance even with small sample sizes (N=60).
  • CSs were computed for Human Connectome Project working memory task data, identifying reliable brain regions with %BOLD change.

Conclusions:

  • Spatial Confidence Sets offer a more robust approach to fMRI analysis than traditional mass-univariate methods.
  • This method enhances the reliability of effect size localization and provides confidence intervals for cluster characteristics.
  • The findings have implications for accurately identifying brain regions with reliable BOLD changes in neuroimaging studies.