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Related Concept Videos

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 Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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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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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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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
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Hidden population size estimation from respondent-driven sampling: a network approach.

Forrest W Crawford1, Jiacheng Wu1, Robert Heimer2

  • 1Department of Biostatistics.

Journal of the American Statistical Association
|March 5, 2019
PubMed
Summary
This summary is machine-generated.

Estimating hidden populations, like people who inject drugs, is challenging. This study introduces a new network-based method using respondent-driven sampling (RDS) data to improve population size estimation.

Keywords:
hidden populationinjection drug usenetwork inferencepopulation size

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Area of Science:

  • Epidemiology
  • Demography
  • Public Health Research
  • Network Science

Background:

  • Estimating the size of stigmatized or hard-to-reach populations presents significant challenges in public health.
  • Traditional methods like capture-recapture and multiplier methods often fail due to the impossibility of random sampling in these populations.

Purpose of the Study:

  • To develop and validate a novel method for estimating the size of hidden populations using network data generated by respondent-driven sampling (RDS).
  • To address the limitations of existing methods by leveraging social network structures inherent in RDS.

Main Methods:

  • Utilizing network data derived from the respondent-driven sampling (RDS) recruitment process, including recruitment chains and subject network degrees.
  • Employing a computationally efficient Bayesian approach to infer missing network connections and estimate population size.
  • Validating the proposed method with simulated data before applying it to a real-world scenario.

Main Results:

  • The study demonstrates that RDS network data provides valuable information for estimating the size of unobserved individuals within a target population.
  • The Bayesian method effectively integrates recruitment chain and network degree information to improve population size estimates.
  • The technique was successfully applied to estimate the population size of people who inject drugs in St. Petersburg, Russia.

Conclusions:

  • Network data from respondent-driven sampling (RDS) offers a powerful tool for estimating hidden population sizes.
  • The developed Bayesian method provides a computationally efficient and effective approach for this estimation.
  • This research contributes a novel methodology for epidemiological and public health research on hard-to-reach groups.