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相关概念视频

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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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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Sampling Plans01:23

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.
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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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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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.
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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相关实验视频

Updated: Feb 25, 2026

Sampling Soils in a Heterogeneous Research Plot
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Sampling Soils in a Heterogeneous Research Plot

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通过对集群随机试验的适当分析,使用标准错误估计有效的样本大小.

Anders Granholm1

  • 1Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Journal of clinical epidemiology
|February 23, 2026
PubMed
概括
此摘要是机器生成的。

新的方法可以估计集群随机试验 (CRT) 的有效样本大小 (ESS),而不需要集群内相关系数 (ICC). 这些方法改善了CRT的解释和证据合成.

关键词:
分析 分析 分析临床试验是指临床试验中的临床试验.集群随机试验是指集群随机试验.集群集成是指集群集成.有效的样本大小有效的样本大小.这是一个元分析.

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相关实验视频

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科学领域:

  • 生物统计学 生物统计学
  • 临床试验方法论 临床试验方法论

背景情况:

  • 集群随机试验 (CRT) 由于集群内相关性,通常比单独随机试验的信息少,导致有效样本大小 (ESS) 较低.
  • 在分析中忽视聚类可能会导致偏见的估计和过于精确的统计结果 (例如,小的p值,狭窄的置信区间).
  • 使用集群内相关系数 (ICC) 估计ESS是常见的,但ICC经常是未知的,必须假设.

研究的目的:

  • 介绍和评估两种用于CRT中估计ESS的新方法.
  • 为分析CRT数据提供实用工具,而不需要预先规定的ICC.

主要方法:

  • 开发了两种不同的方法来估计ESS,使用来自集群意识分析的标准错误.
  • 方法1尺度以集群意识与简单分析差异的比率计算.
  • 方法2采用优化程序来调整事件比例或群体平均值.

主要成果:

  • 这两种方法都成功地防止了CRT组级总结数据分析的过度精度.
  • 第二种方法还通过忽略分析中的聚类来纠正潜在偏差.
  • 使用三个CRT样本的数据进行了比较,与没有考虑集群的分析进行了比较.

结论:

  • 提出的方法为解释和合成CRT证据提供了有价值的工具.
  • 这些方法即使在没有报告ICC的情况下也适用,只要进行适当的集群意识分析.