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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

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Related Experiment Video

Updated: Jun 18, 2026

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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A valid and reliable belief elicitation method for Bayesian priors.

Sindhu R Johnson1, George A Tomlinson, Gillian A Hawker

  • 1Division of Rheumatology, Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, Ontario M5T 2S8, Canada. Sindhu.Johnson@uhn.on.ca

Journal of Clinical Epidemiology
|November 21, 2009
PubMed
Summary

This study found a new method for eliciting expert beliefs for Bayesian priors is feasible, valid, and reliable. This approach can enhance Bayesian clinical studies by formally incorporating prior knowledge.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Decision Making

Background:

  • Bayesian inference formally incorporates prior beliefs into treatment effect analyses using probability distributions (priors).
  • Various methods exist for eliciting expert beliefs for Bayesian studies, but their measurement properties are infrequently evaluated.
  • Evaluating the psychometric properties of belief elicitation methods is crucial for their reliable application in research.

Purpose of the Study:

  • To assess the feasibility, validity, and reliability of a novel belief elicitation method for generating Bayesian priors.
  • To determine if the method is practical for use in clinical research settings.
  • To establish the psychometric soundness of the elicitation technique.

Main Methods:

  • A single-center, cross-sectional study involving academic specialists treating pulmonary hypertension patients.
  • Participants used a novel method involving adhesive dots ('chips') in 'bins' on a line to express probabilities of 3-year survival with and without warfarin.
  • The study evaluated feasibility, face validity, construct validity, and test-retest reliability of the elicitation method.

Main Results:

  • The belief elicitation method was found to be feasible, with 92% of participants reporting face validity and 83% finding questions clear.
  • The response option was easy to use for 92% of participants, with a median completion time of 10 minutes.
  • Internal validity showed moderate agreement (weighted kappa=0.54-0.57), and test-retest reliability was high (intraclass correlation coefficient=0.93).

Conclusions:

  • The developed method for belief elicitation for Bayesian priors demonstrates feasibility, validity, and reliability.
  • This method is suitable for application in Bayesian clinical studies, enhancing the incorporation of prior expert knowledge.
  • The findings support the use of this technique to improve the rigor of Bayesian analyses in medical research.