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Related Concept Videos

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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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.
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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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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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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.
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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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Some methods to improve the utility of conditioned Latin hypercube sampling.

Brendan P Malone1,2, Budiman Minansy2, Colby Brungard3

  • 1CSIRO, Agriculture and Food, Canberra, ACT, Australia.

Peerj
|March 5, 2019
PubMed
Summary

This study enhances the conditioned Latin hypercube sampling (cLHS) algorithm for soil science surveys. It provides practical solutions for optimizing sample size and site selection, improving spatial data collection.

Keywords:
Conditioned Latin HypercubeDigital soil mappingFieldworkLegacy soil dataOptimizationPedometricsSample optimizationSamplingSoil samplingSoil survey

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Soil Science

Background:

  • Conditioned Latin hypercube sampling (cLHS) is vital for spatial surveys of natural phenomena like soils.
  • Field scientists encounter challenges with cLHS, including sample size optimization and site accessibility.
  • Existing methods for addressing these cLHS challenges are fragmented.

Purpose of the Study:

  • To consolidate and extend solutions for common field scientist problems encountered when using cLHS.
  • To provide practical guidance and R scripts for wider adoption of cLHS.
  • To improve the efficiency and effectiveness of spatial sampling in soil science.

Main Methods:

  • The study collates, summarizes, and extends existing solutions for cLHS.
  • It addresses specific issues: optimizing sample size, re-locating inaccessible sites, and incorporating existing data.
  • R scripts are provided to implement the proposed solutions.

Main Results:

  • Practical solutions are presented for optimizing sample size in cLHS.
  • Methods for re-locating sampling sites when initial locations are inaccessible are detailed.
  • A framework is provided to prioritize under-sampled areas using existing sample data.

Conclusions:

  • The developed solutions and R scripts facilitate broader application of cLHS.
  • This work enhances the utility of cLHS for investigating soil spatial variation.
  • Improved spatial sampling strategies will lead to better understanding of natural phenomena.