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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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Random Sampling Method01:09

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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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Sampling Methods: Overview01:06

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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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Stratified Sampling Method01:16

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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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Convenience Sampling Method00:55

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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.
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Risk-Based Sampling: I Don't Want to Weight in Vain.

Mark R Powell1

  • 1U.S. Department of Agriculture, Office of Risk Assessment and Cost Benefit Analysis, 1400 Independence Ave., SW (MS 3811), Washington, DC, 20250, USA. mpowell@oce.usda.gov.

Risk Analysis : an Official Publication of the Society for Risk Analysis
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PubMed
Summary

Risk-based sampling for food safety, animal, and plant health faces challenges similar to financial portfolio analysis. Simple allocation methods may outperform complex optimization due to estimation errors, improving resource allocation efficiency.

Keywords:
Risk-based samplingsanitary and phytosanitary risk

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Area of Science:

  • Agricultural economics
  • Food safety science
  • Risk management

Background:

  • Risk-based sampling is crucial for efficient allocation of inspection and surveillance resources in food safety, animal, and plant health.
  • Modern portfolio theory, based on mean-variance optimization, provides a framework for resource allocation challenges.
  • Estimation error in portfolio optimization leads to suboptimal and unstable allocations.

Purpose of the Study:

  • To explore the challenges of risk-based sampling allocation, drawing parallels with financial portfolio analysis.
  • To investigate the impact of estimation uncertainty on the effectiveness of sampling strategies.
  • To illustrate the implications of portfolio optimization challenges for risk-based lot inspection.

Main Methods:

  • Analogy drawn between risk-based sampling allocation and financial portfolio optimization.
  • Consideration of estimation error and its impact on optimization.
  • Simulation modeling of lot inspection for a small, heterogeneous group of producers.

Main Results:

  • Complex portfolio optimization methods can suffer from estimation errors, leading to unstable and inaccurate allocations.
  • Simple diversification heuristics, like equal allocation, can exhibit superior out-of-sample performance compared to complex methods.
  • The estimation window required for true optimization may be impractically long, especially with many assets.

Conclusions:

  • Estimation uncertainty is a significant challenge in applying portfolio optimization principles to risk-based sampling.
  • Heuristic-based sampling strategies may offer more robust and practical solutions than complex optimization models.
  • Findings have implications for improving the efficiency and reliability of food safety and agricultural surveillance programs.