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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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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.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Prevalence and Incidence01:08

Prevalence and Incidence

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In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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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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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Related Experiment Video

Updated: Oct 17, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Bayesian inference of population prevalence.

Robin Aa Ince1, Angus T Paton1, Jim W Kay2

  • 1School of Psychology and Neuroscience, University of Glasgow, Glasgow, United Kingdom.

Elife
|October 6, 2021
PubMed
Summary

This study introduces a new Bayesian method for estimating effect prevalence in populations, offering a quantitative alternative to traditional null hypothesis significance testing (NHST) for improved replicability in neuroscience and psychology.

Keywords:
generalisationhumaninferenceneuroscienceprevalencestatistics

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Area of Science:

  • Neuroscience
  • Psychology
  • Neuroimaging

Background:

  • Null hypothesis significance testing (NHST) of the population mean is prevalent in neuroscience, psychology, and neuroimaging.
  • Current methods often lack population-level inference, especially in studies with small sample sizes.
  • Replicability is a significant challenge in these scientific fields.

Purpose of the Study:

  • To propose a novel Bayesian method for estimating population prevalence of effects.
  • To offer advantages over traditional population mean NHST.
  • To provide a quantitative population-level inference for studies with limited participant numbers.

Main Methods:

  • Developed a Bayesian approach to estimate population prevalence based on individual participant NHST.
  • Applied the method to address limitations in small-sample studies like psychophysics and precision imaging.

Main Results:

  • The Bayesian prevalence method provides a quantitative population estimate with associated uncertainty.
  • This approach moves beyond binary inferences common in NHST.
  • The method is broadly applicable across neuroscience, psychology, and neuroimaging.

Conclusions:

  • Bayesian prevalence offers a robust alternative to population mean NHST.
  • The method enhances population-level inference, particularly for small-sample studies.
  • Focusing on individual effects aids in addressing replicability issues in scientific research.