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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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Bootstrapping01:24

Bootstrapping

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 small or...

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A Tactile Automated Passive-Finger Stimulator (TAPS)
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Bootstrap variance estimators for the parameters of small-sample sensory-performance functions.

D H Foster1, W F Bischof

  • 1Department of Communication and Neuroscience, University of Keele, Staffordshire, England.

Biological Cybernetics
|January 1, 1987
PubMed
Summary

The bootstrap method, a resampling technique, offers a superior approach to estimating sensory-performance function parameters compared to traditional incremental methods. This statistical innovation provides more accurate and efficient results, especially when other methods falter.

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Area of Science:

  • Statistics
  • Psychophysics
  • Biostatistics

Background:

  • Estimating sensory-performance functions is crucial in psychophysics.
  • Traditional methods like probit-transformation can be unreliable with small datasets.
  • The bootstrap method offers a robust alternative for variance estimation.

Purpose of the Study:

  • To evaluate the efficacy of the bootstrap method for estimating sensory-performance function parameters.
  • To compare the bootstrap estimator's performance against the classical incremental method.
  • To determine the suitability of the bootstrap method for small experimental datasets.

Main Methods:

  • Application of the bootstrap method for resampling experimental data.
  • Estimation of standard deviation for midpoint and spread in sensory-performance functions.
  • Monte Carlo simulations to assess estimator performance.
  • Comparison with the classical 'combination-of-observations' or incremental method.

Main Results:

  • The bootstrap method demonstrated significantly smaller percentage biases.
  • Bootstrap estimators exhibited greater efficiencies compared to the incremental method.
  • The bootstrap method proved superior in performance for the tested scenarios.

Conclusions:

  • The bootstrap method is a powerful and effective tool for analyzing sensory-performance data.
  • It is particularly advantageous when traditional asymptotic methods are unreliable.
  • The bootstrap method provides more accurate and efficient parameter estimations.