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What are Estimates?01:06

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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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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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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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.
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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Conjoint psychometric field estimation for bilateral audiometry.

Dennis L Barbour1, James C DiLorenzo2,3, Kiron A Sukesan2,3

  • 1Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University, 1 Brookings Drive, Box 1097, St. Louis, MO, 63130, USA. dbarbour@wustl.edu.

Behavior Research Methods
|June 28, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces machine learning for faster, more comprehensive behavioral testing. It uses a new method to estimate hearing ability (audiometry) in both ears simultaneously, reducing test time.

Keywords:
AudiometryHearingPerceptual testingPsychometric functionPsychophysics

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Area of Science:

  • Cognitive science
  • Machine learning
  • Psychophysics

Background:

  • Traditional behavioral tests are lengthy and sequential, limiting comprehensive evaluation.
  • Existing methods often require extensive testing time for perceptual or cognitive domains.
  • Sequential testing in domains like audiometry is time-consuming and can be inefficient.

Purpose of the Study:

  • To develop a more efficient and comprehensive method for behavioral assessment using machine learning.
  • To introduce a novel conjoint psychometric function estimation procedure for bilateral audiometry.
  • To reduce the time required for behavioral testing while maintaining accuracy.

Main Methods:

  • Utilized active machine-learning kernel methods for behavioral assessment.
  • Developed a bilateral audiogram approach by conjoining input domains of both ears.
  • Implemented a conjoint psychometric function estimation procedure.

Main Results:

  • The new method significantly reduces testing time compared to sequential disjoint estimators.
  • Accurate estimation of hearing thresholds in both ears was achieved simultaneously.
  • Demonstrated the utility of machine learning in simplifying and accelerating audiometry.

Conclusions:

  • Machine learning offers flexible and efficient tools for thorough investigation of cognitive and perceptual processes.
  • The conjoint audiogram estimation procedure is a significant advancement in psychometric function estimation.
  • This approach has the potential to revolutionize behavioral testing by reducing time and increasing comprehensiveness.