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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Sampling Methods: Sample Types01:18

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Sampling materials are classified into three main types: solid, liquid, and gas.
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Sampling Plans

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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.
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Sample Handling

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Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
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An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering
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Doubly balanced samples with dynamic sample sizes.

Blair Robertson1, Chris Price1, Marco Reale1

  • 1School of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.

Biometrics
|February 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new objective function for dynamic assignment sampling (DAS) to create doubly balanced samples. The method ensures samples are both spatially balanced and balanced on auxiliary variables, outperforming existing designs.

Keywords:
environmental samplinglinear assignmentsover-samplingspatial balance

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Area of Science:

  • Ecology
  • Environmental Science
  • Spatial Statistics

Background:

  • Spatial sampling designs are crucial for precise population parameter estimation.
  • Spatially balanced designs are effective due to positive spatial associations in environmental data.
  • Dynamic Assignment Sampling (DAS) is a recent design for drawing spatially balanced samples.

Purpose of the Study:

  • To propose a novel objective function for DAS.
  • To achieve doubly balanced samples (spatially and on auxiliary variables).
  • To compare the new method with existing fixed sample size designs.

Main Methods:

  • Developed a new objective function for DAS.
  • Required only a measure of distance between population units.
  • Generated master or over-samples using the new objective function.

Main Results:

  • The proposed method successfully generates spatially balanced, balanced, or doubly balanced samples.
  • The new DAS objective function performs favorably compared to established designs.
  • Demonstrated application using total aboveground biomass in Eastern Amazonia, Brazil.

Conclusions:

  • The new objective function enhances DAS for creating robust, doubly balanced samples.
  • This approach offers improved precision for estimating population parameters in spatial studies.
  • The method is applicable to large-scale environmental studies requiring spatial and auxiliary variable balancing.