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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

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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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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

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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

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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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Dissolution kinetics, an essential aspect of oral drug delivery, is significantly influenced by the drug's particle size. According to the Noyes-Whitney dissolution model, the dissolution rate correlates directly with the drug's surface area. The larger the surface area, the higher the drug's solubility in water, leading to a faster drug dissolution rate. Reducing particle size increases the effective surface area, enhancing the dissolution process. Micronization and nanosizing are...
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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Estimating recent migration and population-size surfaces.

Hussein Al-Asadi1,2, Desislava Petkova3, Matthew Stephens2,4

  • 1Evolutionary Biology, University of Chicago, Chicago, Illinois, United States of America.

Plos Genetics
|January 15, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to map historical population sizes and migration rates using genetic similarity. The approach reveals complex genetic diversity patterns over time, offering insights into population histories.

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Area of Science:

  • Population Genetics
  • Molecular Ecology
  • Evolutionary Genetics

Background:

  • Genetic similarity typically decays with geographic distance, but this relationship is complex and varies across space and time.
  • Understanding these patterns is crucial for molecular ecology, conservation, and human genetics.
  • Inferring maps of migration rates across space and time can reveal intricate population histories.

Purpose of the Study:

  • To develop a novel method for inferring time-varying population sizes and migration rates.
  • To analyze genetic similarity based on haplotype sharing to reconstruct past demographic events.
  • To provide a more detailed understanding of population dynamics over extended periods.

Main Methods:

  • Utilized a matrix of genetic similarity between individuals, measured by identity-by-descent tracts (haplotype sharing).
  • Varied the length of shared segments to estimate parameters associated with different historical time periods.
  • Applied the method to simulated data and a real-world dataset of contemporary European individuals (POPRES).

Main Results:

  • The method successfully inferred time-varying population sizes and migration rates in simulations.
  • It identified demographic changes not detectable by methods ignoring haplotypic structure.
  • Analysis of the POPRES dataset revealed recent European population structure and growth over the last ~3,000 years.

Conclusions:

  • The developed method offers a powerful tool for reconstructing historical population dynamics using genetic data.
  • It enhances the analysis of genetic diversity by incorporating haplotype information for finer temporal resolution.
  • Provides a detailed view of recent European demographic history, including population structure and expansion.