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Related Concept Videos

Cluster Sampling Method01:20

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 Plans01:23

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...
Stratified Sampling Method01:16

Stratified 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. 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...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

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Related Experiment Video

Updated: Jun 12, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Exact inference for complex clustered data using within-cluster resampling.

Dean Follmann1, Michael Fay

  • 1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland 20892, USA. dfollmann@niaid.nih.gov

Journal of Biopharmaceutical Statistics
|May 25, 2010
PubMed
Summary
This summary is machine-generated.

This study presents exact permutation methods to address clustered data with complex correlations. The novel approach, a permutation extension of within-cluster resampling (WCR), provides a robust reference distribution for accurate statistical inference.

Related Experiment Videos

Last Updated: Jun 12, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Clustered data with arbitrary within-cluster correlation presents challenges for traditional statistical methods.
  • Existing resampling techniques may not adequately address complex correlation structures within independent clusters.

Purpose of the Study:

  • To introduce exact permutation methods for statistical inference in the presence of clustered data.
  • To develop a permutation extension of within-cluster resampling (WCR) that handles arbitrary within-cluster correlations.

Main Methods:

  • Randomly selecting one data point from each cluster to create independent datasets.
  • Calculating test statistics and support points for all possible permutations of these independent datasets.
  • Averaging support points to create a reference distribution for the averaged test statistic, forming the WCR permutation method.

Main Results:

  • The proposed WCR permutation method provides an exact inference approach for clustered data.
  • Both exact and Monte Carlo versions of the method were developed and applied to various datasets.
  • The method effectively handles nuisance within-cluster correlation, enabling exact inference in general settings.

Conclusions:

  • The WCR permutation method offers a valid and efficient approach for statistical inference with clustered data.
  • This technique is particularly useful when within-cluster correlation is a concern and exact inference is required.
  • The method extends permutation principles to within-cluster resampling, broadening its applicability.