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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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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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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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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Systematic Sampling Method01:17

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling

Sebastien J Puechmaille1,2

  • 1Zoology Institute, University of Greifswald, Soldmannstraße 14, D-17489, Greifswald, Germany.

Molecular Ecology Resources
|February 10, 2016
PubMed
Summary

Uneven sample sizes in population genetics studies can distort results, merging distinct groups or splitting single populations. New methods and subsampling improve accuracy in detecting true population structure.

Keywords:
clustering algorithmgenetic differentiationhierarchical structuresampling schemestructure software

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Conservation biology

Background:

  • Accurate population structure inference is crucial for ecological and evolutionary studies.
  • The STRUCTURE program is widely used for population structure analysis but its robustness to uneven sampling is unknown.
  • Uneven sample sizes are common in real-world genetic data.

Purpose of the Study:

  • To investigate the impact of uneven sample sizes on population structure inference.
  • To develop and test new methods for improved population structure detection.
  • To evaluate a subsampling strategy for mitigating sampling bias.

Main Methods:

  • Simulated and empirical microsatellite data were used to test population structure inference under uneven sampling.
  • Four novel supervised methods for cluster detection were developed and compared to existing approaches.
  • A subsampling strategy was implemented to address sampling unevenness.

Main Results:

  • Uneven sampling led to incorrect hierarchical structure inferences and underestimated the number of subpopulations.
  • Reduced sampling in distinct subpopulations caused them to merge, while oversampling split homogeneous populations.
  • The new supervised methods outperformed existing ones, especially with unevenly sampled data.

Conclusions:

  • Sampling evenness is critical for accurate population structure analysis.
  • Addressing sampling bias significantly improves the reliability of genetic structure inferences.
  • The developed methods and subsampling strategy offer improved tools for population geneticists.