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Related Concept Videos

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.
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...
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Cluster Sampling Method01:20

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.
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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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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Convenience Sampling Method00:55

Convenience 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. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Systematic Sampling Method01:17

Systematic 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. 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...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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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. 
In analytical chemistry, the choice of...
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Related Experiment Video

Updated: Dec 16, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Finding hotspots: development of an adaptive spatial sampling approach.

Ricardo Andrade-Pacheco1, Francois Rerolle1, Jean Lemoine2

  • 1Global Health Group, University of California, San Francisco, San Francisco, USA.

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|July 4, 2020
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Summary
This summary is machine-generated.

This study introduces adaptive sampling to efficiently identify disease hotspots, outperforming traditional methods. This approach can achieve similar results to random sampling with fewer resources, improving public health surveillance.

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Area of Science:

  • Public Health
  • Epidemiology
  • Geospatial Analysis

Background:

  • Identifying disease hotspots is crucial for public health.
  • Geospatial modeling can predict disease locations using environmental data.
  • Survey design optimization for hotspot identification has been overlooked.

Purpose of the Study:

  • To introduce an adaptive sampling scheme for optimizing survey design.
  • To identify disease hotspot locations where prevalence exceeds a specific threshold.
  • To improve the efficiency of disease surveillance.

Main Methods:

  • Utilized Bayesian optimization theory for adaptive sample selection.
  • Developed an adaptive sampling scheme to pinpoint disease hotspots.
  • Conducted simulation studies using schistosomiasis and lymphatic filariasis data from four countries.

Main Results:

  • Adaptive sampling demonstrated superior performance in identifying disease hotspots.
  • Achieved comparable results to random sampling with a significantly reduced sample size.
  • The proposed method proved effective across various scenarios.

Conclusions:

  • Adaptive sampling is a more efficient strategy for disease hotspot identification.
  • This method can reduce the resources needed for effective disease surveillance.
  • Optimized survey design is essential for accurate geospatial disease modeling.