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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

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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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Sample Size Calculation01:19

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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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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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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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Sampling Plans01:23

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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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Related Experiment Video

Updated: Nov 5, 2025

Sampling Soils in a Heterogeneous Research Plot
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Estimating sampling uncertainty - how many duplicate samples are needed?

Analytical Methods Committee Amctb No

    Analytical Methods : Advancing Methods and Applications
    |May 14, 2021
    PubMed
    Summary

    Understanding measurement uncertainty from sampling is vital for effective sampling programs. The primary source of this uncertainty is often the heterogeneity of the sampling target, impacting data interpretation.

    Area of Science:

    • Environmental Science
    • Analytical Chemistry
    • Geoscience

    Background:

    • Measurement uncertainty is critical for designing effective sampling strategies and interpreting results.
    • Lack of homogeneity in the sampling target (spatial or temporal) is typically the largest contributor to uncertainty.
    • Analytical and sampling processes also introduce significant sources of variation.

    Purpose of the Study:

    • To highlight the importance of understanding measurement uncertainty in sampling.
    • To identify the primary sources of sampling uncertainty.
    • To emphasize the need for considering heterogeneity in sampling design.

    Main Methods:

    • This brief discusses the conceptual framework of measurement uncertainty in sampling.
    • It reviews common sources of variation in the sampling process.

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  • Focuses on the impact of target material heterogeneity.

    • Main Results:

    • Heterogeneity of the sampling target is identified as the principal source of measurement uncertainty.
    • Both spatial and temporal heterogeneity significantly influence overall uncertainty.
    • Analytical and sampling procedures contribute additional, though often secondary, sources of variation.

    Conclusions:

    • Accurate assessment of measurement uncertainty is essential for robust sampling programs.
    • Addressing target heterogeneity is key to minimizing sampling uncertainty.
    • A comprehensive understanding of all variation sources improves data reliability.