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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Systematic Sampling Method01:17

Systematic Sampling Method

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.
Systematic sampling is one of the simplest methods...
Sampling Plans01:23

Sampling Plans

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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...

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Related Experiment Video

Updated: Jun 28, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Characterizing populations of individuals using pooled samples.

Samuel P Caudill1

  • 1Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. spc1@cdc.gov

Journal of Exposure Science & Environmental Epidemiology
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

Pooling biological samples for biomonitoring significantly cuts costs and can improve accuracy. This study introduces a statistical method to optimize pooled-sample design for better population distribution characterization.

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Sampling Soils in a Heterogeneous Research Plot
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Sampling Soils in a Heterogeneous Research Plot

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Area of Science:

  • Environmental health
  • Toxicology
  • Public health

Background:

  • Biomonitoring assesses populations by measuring biological compounds in individuals.
  • Current methods can be costly due to individual sample analyses.
  • Pooling samples offers a potential cost-reduction strategy.

Purpose of the Study:

  • To develop a statistical method for population distribution characterization using pooled samples.
  • To determine an optimal pooled-sample design for cost-efficient biomonitoring.
  • To evaluate the precision and bias of pooled versus individual sample analysis.

Main Methods:

  • Statistical modeling to relate interindividual variance to pooled sample variation.
  • Simulation experiments to identify optimal pooled-sample designs.
  • Analysis of population distribution parameters from pooled samples.

Main Results:

  • A statistical method was developed to characterize population distributions from pooled samples.
  • Optimal pooled-sample designs were identified based on subpopulation number and sample availability.
  • Pooled sampling can be more cost-efficient and yield more precise parameter estimates.

Conclusions:

  • Pooled-sample designs offer a cost-effective approach to biomonitoring.
  • The proposed statistical method enables accurate population distribution characterization.
  • Optimized pooling strategies can enhance the precision and reduce bias in biomonitoring data.