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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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Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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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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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 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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Evaluation of the Impact of Protein Aggregation on Cellular Oxidative Stress in Yeast
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Confidence Sets for Cohen's d effect size images.

Alexander Bowring1, Fabian J E 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
|November 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces spatial Confidence Sets (CSs) for task-fMRI data, improving statistical inference by quantifying activation reliability and spatial uncertainty. The method accurately localizes brain regions with significant effect sizes, even in large-scale studies.

Keywords:
Cohen’s dConfidence setsEffect sizesTask fmrifMRI

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Area of Science:

  • Neuroimaging
  • Statistical Inference
  • Brain Mapping

Background:

  • Task-fMRI statistical methods face limitations in detecting meaningful signals and quantifying spatial uncertainty.
  • Large-scale neuroimaging studies are susceptible to the 'null hypothesis fallacy', leading to spurious significant findings.
  • Current cluster inference methods lack information on the reliability and spatial variability of brain activation.

Purpose of the Study:

  • To develop spatial Confidence Sets (CSs) for Cohen's d effect size images in task-fMRI.
  • To address limitations in current statistical inference by providing measures of spatial uncertainty and effect size reliability.
  • To improve the localization of activated brain regions in neuroimaging studies.

Main Methods:

  • Developed spatial Confidence Sets (CSs) for thresholded Cohen's d effect size images.
  • Expanded existing CS theory using recent bootstrapping literature for enhanced statistical power.
  • Validated the method using 2D and 3D Monte Carlo simulations and applied it to Human Connectome Project working memory task-fMRI data.

Main Results:

  • Spatial CSs provide upper and lower bounds for confidence statements on effect sizes.
  • The method demonstrates accuracy in sample sizes as low as N=60, with reliable Cohen's d responses identified.
  • Comparison with traditional voxelwise inference highlights improved activation localization using CSs.

Conclusions:

  • Spatial Confidence Sets offer a robust approach to statistical inference in task-fMRI.
  • The developed method enhances the understanding of effect size magnitude and reliability in neuroimaging.
  • CSs represent a significant advancement for precise activation localization in brain mapping studies.