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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

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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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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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Explicit memories, also known as declarative memories, are consciously remembered, recalled, and reported. Studying for a chemistry exam involves material that will become part of explicit memory. There are two types of explicit memory: episodic and semantic.
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Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
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Bayesian estimation of partial population continuity using ancient DNA and spatially explicit simulations.

Nuno Miguel Silva1, Jeremy Rio1, Susanne Kreutzer2

  • 1AGP Lab Department of Genetics & Evolution - Anthropology Unit University of Geneva Geneva Switzerland.

Evolutionary Applications
|October 23, 2018
PubMed
Summary

Ancient DNA analysis reveals population shifts during the Neolithic transition in Central Europe. New methods estimate genetic continuity, showing farmer-hunter-gatherer admixture and gene flow from Anatolian hunter-gatherers.

Keywords:
Neolithic transition in Europeancient DNAgenomewide autosomal datamtDNApartial population continuitypopulation geneticsserial coalescentspatial explicit simulations

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Area of Science:

  • Paleogenetics
  • Population Genetics
  • Archaeogenetics

Background:

  • Ancient DNA (aDNA) from osteological material offers direct insights into past human genetic diversity.
  • Investigating population continuity in settlement history often relies on aDNA analysis.
  • Existing serial coalescent methods can reject full continuity but struggle with partial continuity due to admixture.

Purpose of the Study:

  • To extend a novel method for estimating the proportion of genetic continuity between populations using ancient genetic samples.
  • To apply this method to understand the Neolithic transition in Central Europe.
  • To refine the assessment of genetic contributions from immigrant farmers and local hunter-gatherers.

Main Methods:

  • Development and application of a new method explicitly considering spatial and temporal population dynamics.
  • Analysis of ancient DNA samples from Central Europe to estimate genetic continuity proportions.
  • Joint estimation of demographic parameters, including population density and migration rates.

Main Results:

  • Confirmed rejection of full genetic continuity during the Central European Neolithic transition.
  • Estimated the relative genetic contributions of incoming farmers and indigenous hunter-gatherers.
  • Identified assimilation of hunter-gatherer genes by migrating farmers from populations outside Anatolia, a finding missed by prior methods.
  • Characterized pre-Neolithic hunter-gatherers as having low density and low migration rates.

Conclusions:

  • The new method provides a more nuanced understanding of population continuity than previous approaches.
  • It accurately estimates the proportion of genetic continuity and admixture, crucial for interpreting population history.
  • The findings offer significant insights into the complex genetic landscape of the Neolithic transition in Central Europe.
  • This approach is valuable for analyzing the growing volume of ancient DNA data across various species.