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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...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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...
Convenience Sampling Method00:55

Convenience 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.
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...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

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Related Experiment Video

Updated: May 22, 2026

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

Siting samplers to minimize expected time to detection.

Travis Walter1, David M Lorenzetti, Michael D Sohn

  • 1Energy Analysis and Environmental Impacts Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mail Stop 90R3058, Berkeley, CA 94720, USA. mdsohn@lbl.gov

Risk Analysis : an Official Publication of the Society for Risk Analysis
|May 4, 2012
PubMed
Summary
This summary is machine-generated.

This study optimizes indoor chemical and biological detection networks using a probabilistic approach. The new method, focusing on rapid sampler data, minimizes detection time for faster warnings.

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Last Updated: May 22, 2026

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

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Published on: December 29, 2021

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Area of Science:

  • Environmental Science
  • Chemical Engineering
  • Public Health

Background:

  • Previous research focused on slow-returning samplers for "detect to treat" strategies.
  • Rapid data return from samplers enables a shift towards "detect to warn" protocols.

Purpose of the Study:

  • To develop a probabilistic framework for designing optimal indoor sensor networks.
  • To minimize the expected time to detect chemical or biological releases using rapid-response samplers.

Main Methods:

  • A probabilistic approach was used to design indoor sampler networks.
  • The method was applied to a model of a large, commercial building.
  • Optimization considered uncertain release locations, source terms, and sampler capabilities.

Main Results:

  • The study demonstrates optimal "detect to warn" architectures for rapid-response samplers.
  • The approach effectively minimizes the expected time to detection.
  • Validated for a real-world building model with variable parameters.

Conclusions:

  • Rapid-response sampler networks can significantly reduce detection times for hazardous releases.
  • The probabilistic framework provides a robust method for designing effective "detect to warn" systems.
  • Findings offer insights into general sampler placement strategies for early warning systems.