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Sampling Plans01:23

Sampling Plans

810
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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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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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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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.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

438
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Related Experiment Video

Updated: Dec 24, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Pooled variable scaling for cluster analysis.

Jakob Raymaekers1, Ruben H Zamar2

  • 1Department of Mathematics, KU Leuven, Leuven 3001, Belgium.

Bioinformatics (Oxford, England)
|April 14, 2020
PubMed
Summary
This summary is machine-generated.

A new pooled variance-based scaling method improves cluster analysis by maintaining variable importance. This safe and efficient approach is crucial for bioinformatics and medical research, especially for high-dimensional genomic data.

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Area of Science:

  • Bioinformatics
  • Medical Sciences Research
  • Cluster Analysis

Background:

  • Clustering methods often lack scale invariance due to Euclidean distances.
  • Scale-invariant methods can lose invariance with regularization or variable selection, making results sensitive to measurement units.

Purpose of the Study:

  • To develop a safe and efficient scaling procedure for cluster analysis.
  • To address the sensitivity of clustering results to measurement units in bioinformatics and medical research.

Main Methods:

  • Proposed a novel scaling approach based on pooled variance prior to cluster analysis.
  • Evaluated the method through extensive simulations and real-data examples, including a high-dimensional genomic dataset.

Main Results:

  • The proposed scaling method avoids dampening informative variables, unlike standard deviation or range scaling.
  • Demonstrated the safety and general utility of the new scaling approach.
  • Successfully applied the method to cluster gene expression data from breast cancer cell tissues.

Conclusions:

  • The pooled variance-based scaling method offers a robust solution for scale-invariant cluster analysis.
  • This approach is particularly beneficial for high-dimensional data in bioinformatics and medical research.
  • An R implementation is available for practical application.