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Sampling Plans

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...

Convenience Sampling Method

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...

Cluster Sampling Method

Cluster Sampling Method

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...

Sampling Methods: Overview

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...

Random Sampling Method

Random 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. 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...

Systematic Sampling Method

Systematic 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. 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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Related Experiment Video

Updated: May 22, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

A Comparison of EPI Sampling, Probability Sampling, and Compact Segment Sampling Methods for Micro and Small

Li-Wei Chao¹, Helena Szrek, Karl Peltzer

¹Population Studies Center, University of Pennsylvania, 3718 Locust Walk, Room 239, Philadelphia, Pennsylvania 19104-6298, U.S.A.

Journal of Development Economics

|May 15, 2012

Summary

Sampling micro- and small-enterprises (MSEs) in developing countries is difficult due to poor registries. This study simulated sampling methods, finding revisits and accurate weights crucial for representative business samples.

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Last Updated: May 22, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience

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Published on: April 13, 2019

Sampling Soils in a Heterogeneous Research Plot

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Published on: January 7, 2019

Microsampling in Targeted Mass Spectrometry-Based Protein Analysis of Low-Abundance Proteins

Microsampling in Targeted Mass Spectrometry-Based Protein Analysis of Low-Abundance Proteins

Published on: January 13, 2023

Area of Science:

Social Sciences
Statistics
Development Studies

Background:

Accurate sampling of micro- and small-enterprises (MSEs) is vital for research and statistical reporting.
Developing countries often lack comprehensive registries, hindering representative MSE sampling.
Existing sampling frames are frequently outdated or nonexistent, posing significant challenges.

Purpose of the Study:

To evaluate the efficiency of different sampling methods for micro- and small-enterprises (MSEs).
To compare traditional probability sampling, compact segment sampling, and the EPI sampling method.
To assess the impact of respondent proximity, at-home selection, and probability weight accuracy on sampling outcomes.

Main Methods:

Computer simulations were employed using a business and non-business census in Tshwane Municipality, South Africa.
Three distinct sampling methodologies were simulated: traditional probability sampling, compact segment sampling, and the Expanded Programme on Immunization (EPI) method.
The study analyzed the influence of proximity selection, at-home respondent selection, and inaccurate probability weights.

Main Results:

Revisits and accurate probability weights significantly improved sample properties.
The proximity selection of respondents had a less pronounced effect on the statistical properties of the samples.
The EPI sampling method showed comparable results to traditional methods under certain conditions.

Conclusions:

Effective sampling of MSEs requires careful consideration of revisits and accurate weighting mechanisms.
The findings offer practical insights for improving statistical data collection on MSEs in data-scarce environments.
Methodological choices in sampling significantly impact the representativeness and reliability of MSE data.