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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Related Experiment Video

Updated: Jan 1, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Cluster failure or power failure? Evaluating sensitivity in cluster-level inference.

Stephanie Noble1, Dustin Scheinost2, R Todd Constable3

  • 1Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA.

Neuroimage
|December 20, 2019
PubMed
Summary
This summary is machine-generated.

Functional MRI (fMRI) cluster correction methods have low sensitivity, failing to detect many true brain activations. New strategies are needed to improve detection power while maintaining accuracy in neuroscience research.

Keywords:
ActivationEmpiricalHCPPowerResamplingSensitivityfMRI

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Task-based functional magnetic resonance imaging (fMRI) is crucial for mapping brain function.
  • Previous research highlighted issues with specificity (false positives) in cluster correction methods.
  • The sensitivity (true positive rate or power) of these methods remains poorly understood.

Purpose of the Study:

  • To assess the sensitivity of standard nonparametric cluster correction in fMRI.
  • To compare the sensitivity of cluster extent correction with Threshold-Free Cluster Enhancement (TFCE).
  • To identify strategies for improving sensitivity in fMRI analysis.

Main Methods:

  • Resampling real fMRI data from the Human Connectome Project (n=480-493) across five tasks.
  • Comparing results from cluster correction against full "ground truth" datasets.
  • Evaluating sensitivity for medium-sized effects (Cohen's d = 0.5).

Main Results:

  • Standard cluster correction demonstrated low sensitivity, detecting less than 20% of medium-sized effects on average.
  • Sensitivity was approximately three times lower with cluster correction compared to no correction.
  • Threshold-Free Cluster Enhancement (TFCE) approximately doubled sensitivity but introduced spatial bias.

Conclusions:

  • Current fMRI activation-mapping may be underestimating true findings due to low sensitivity of cluster correction.
  • There is a critical need to revise fMRI analysis practices to enhance sensitivity.
  • Modern strategies should be adopted to boost sensitivity while preserving specificity.