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Related Concept Videos

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Cluster Sampling Method

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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.
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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.
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Sampling Plans01:23

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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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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. Among the various sampling methods used by...
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Updated: Nov 5, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
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SEQUENTIAL IMPORTANCE SAMPLING FOR MULTIRESOLUTION KINGMAN-TAJIMA COALESCENT COUNTING.

Lorenzo Cappello1, Julia A Palacios1

  • 1Stanford University.

The Annals of Applied Statistics
|May 17, 2021
PubMed
Summary
This summary is machine-generated.

Estimating the size of genealogical tree spaces is crucial for efficient evolutionary analysis. We developed a novel sequential importance sampling method to accurately estimate these sizes, improving computational efficiency for molecular sequence data.

Keywords:
coalescentenumerationsequential importance sampling

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Population genetics

Background:

  • Coalescent models are essential for inferring evolutionary parameters from molecular sequences.
  • Current inferential algorithms struggle to scale with large datasets.
  • Simpler models improve computational efficiency but require knowledge of state-space cardinality.

Purpose of the Study:

  • To develop a method for estimating the cardinality of genealogical tree spaces.
  • To enable informed decisions on appropriate coalescent and mutation model complexity.
  • To address the lack of existing methods for determining state-space cardinality.

Main Methods:

  • A sequential importance sampling algorithm is proposed.
  • The algorithm estimates the cardinality of genealogical tree spaces under varying coalescent resolutions.
  • The method sequentially processes combinatorial constraints from DNA sequence data.

Main Results:

  • The cardinality of different genealogical tree spaces was analyzed using simulations.
  • Settings favoring coarser coalescent resolutions were identified.
  • The method was successfully applied to human mtDNA and beta-globin locus data.

Conclusions:

  • The developed method provides a crucial tool for selecting appropriate models in evolutionary inference.
  • This approach enhances the computational efficiency of analyzing molecular sequence data.
  • Accurate estimation of state-space cardinality facilitates more scalable and effective population genetic studies.