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Related Concept Videos

Sampling Plans01:23

Sampling Plans

155
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...
155
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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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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Sampling Methods: Overview01:06

Sampling Methods: Overview

240
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...
240
Random Sampling Method01:09

Random Sampling Method

10.9K
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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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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Related Experiment Video

Updated: May 13, 2025

Sampling Soils in a Heterogeneous Research Plot
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A partitioned conditioned Latin hypercube sampling method considering spatial heterogeneity in digital soil mapping.

Biao Huang1, Guijian Yang2, Jiancong Lei1

  • 1School of Geographic Sciences, Hunan Normal University, 36 Lushan Road, Changsha, 410081, China.

Scientific Reports
|April 14, 2025
PubMed
Summary

A new partitioned conditioned Latin hypercube sampling (PcLHS) method improves soil organic carbon (SOC) mapping by accounting for spatial variations. This approach enhances prediction accuracy in digital soil mapping for better soil management.

Keywords:
Partitioned conditional Latin hypercube sampling methodSoil organic carbonSpatial heterogeneity

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Area of Science:

  • Digital Soil Science
  • Geostatistics
  • Environmental Modeling

Background:

  • Accurate soil organic carbon (SOC) prediction is vital for digital soil mapping and land management.
  • Traditional sampling methods struggle with spatial heterogeneity, impacting prediction precision and reliability.
  • Environmental variables influence soil properties, but their driving factors vary across subregions.

Purpose of the Study:

  • To introduce and evaluate a novel sampling method, partitioned conditioned Latin hypercube sampling (PcLHS), designed to address spatial heterogeneity in digital soil mapping.
  • To improve the accuracy and reliability of soil organic carbon (SOC) predictions in complex landscapes.

Main Methods:

  • The study proposed the PcLHS method, which integrates regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) for area subdivision.
  • Key environmental variables were identified using Boruta and Variance Inflation Factor (VIF) methods.
  • Conditioned Latin hypercube sampling (cLHS) was applied within each subregion to select training points, forming a comprehensive dataset.

Main Results:

  • PcLHS demonstrated superior performance over traditional methods in a case study of SOC sampling in northeastern France.
  • PcLHS achieved significantly lower root mean square error (RMSE), higher coefficient of determination (R²), and improved concordance correlation coefficient (CCC) compared to other methods.
  • Specifically, PcLHS reduced RMSE by 4-11%, increased R² by 18-46%, and improved CCC by 14-29%.

Conclusions:

  • The findings underscore the critical importance of incorporating spatial heterogeneity into soil sampling designs for accurate digital soil mapping.
  • The PcLHS method is validated as an effective strategy for enhancing SOC prediction accuracy, particularly in heterogeneous environments.
  • This approach offers a robust framework for optimizing soil sampling strategies in diverse landscapes.