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Related Concept Videos

Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Updated: Apr 16, 2026

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Resampling (bootstrapping) the mean: A definite do.

J Peter Rosenfeld1, Emanuel Donchin2

  • 1Department of Psychology, Northwestern University, Evanston, Illinois, USA.

Psychophysiology
|February 27, 2015
PubMed
Summary
This summary is machine-generated.

Bootstrap and permutation methods are both valid for concealed information tests (CITs/GKTs) in psychophysiology. These statistical approaches showed high agreement in synthesized event-related potential (ERP) datasets.

Keywords:
BootstrapCITDeception detectionGKTP300PermutationsResampling techniques

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Area of Science:

  • Psychophysiology
  • Cognitive Neuroscience
  • Forensic Psychology

Background:

  • Statistical methods in diagnostic psychophysiology, particularly for concealed information tests (CITs), also known as guilty knowledge tests (GKTs), are under recent discussion.
  • The appropriateness of bootstrap versus permutation methods for analyzing data in these tests requires clarification.

Purpose of the Study:

  • To review the application of bootstrapping in published CIT/GKT studies.
  • To compare the performance of bootstrap and permutation methods in analyzing synthesized event-related potential (ERP) datasets.

Main Methods:

  • Review of bootstrapping techniques employed in existing CIT/GKT literature, focusing on the estimation of means and correlations.
  • Application of both bootstrapping and permutation methods to synthesized ERP datasets for direct comparison.

Main Results:

  • Bootstrapping methods have been widely and validly used in published CIT/GKT studies for estimating parameters like means and correlations.
  • Bootstrap and permutation methods demonstrated strong agreement, coinciding 98.1% of the time in synthesized ERP data analysis over 24,000 iterations.

Conclusions:

  • Both bootstrapping and permutation methods are reliable and appropriate for diagnostic psychophysiology, especially within the context of CITs/GKTs.
  • The high concordance between methods suggests robustness in statistical findings for concealed information detection.