Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
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...
12.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.7K
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...
7.7K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.3K
Bootstrapping01:24

Bootstrapping

601
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
601
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
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:
5.7K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

General two-parameter distribution: Statistical properties, estimation, and application on COVID-19.

PloS one·2023
Same author

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.

PloS one·2023
Same author

A New Model of Discrete-Continuous Bivariate Distribution with Applications to Medical Data.

Computational and mathematical methods in medicine·2022
Same author

Dynamic spatiotemporal modeling of the infected rate of visceral leishmaniasis in human in an endemic area of Amhara regional state, Ethiopia.

PloS one·2019

相关实验视频

Updated: Jun 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

在精细分层下基于bootstrap的差异估计.

Alexis Habineza1,2, Romanus Odhiambo Otieno3,4, George Otieno Orwa3

  • 1Pan African University, Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi, Kenya.

PloS one
|June 13, 2024
PubMed
概括

这项研究引入了一种基于启动的新型差异估计器,用于调查中的细分分层. 它有效地解决了传统的崩层方法中发现的高估问题,提高了估计准确度.

更多相关视频

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

相关实验视频

Last Updated: Jun 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

科学领域:

  • 调查方法 调查方法
  • 统计推理 统计推理
  • 采样理论 采样理论

背景情况:

  • 样本调查的目的是提供准确的点估计,并通过差异估计量化不确定性.
  • 精细分层将种群划分为小层,确保子组表示,但在小样本大小的情况下复杂化差异估计.
  • 众所周知,用于细分分层的差异估计的传统崩层技术具有偏差,导致过高估计.

研究的目的:

  • 建议在微分层下为总人口提供新的基于引导式的差异估计器.
  • 解决现有方法的局限性,特别是与崩层技术相关的偏差和高估.
  • 调查拟议估计器的属性和性能.

主要方法:

  • 开发一种基于引导式的新型差异估计器,适用于细分分层设计.
  • 对拟议估计器的属性进行理论研究.
  • 经验评估通过模拟研究和现实世界的应用,使用心理健康组织的调查数据.

主要成果:

  • 拟议的基于引导的差异估计器有效地克服了崩层技术的缺点.
  • 模拟研究和实际应用证明了新估计器的良好性能.
  • 新方法在细分分层场景中提供了更准确,更稳定的差异估计.

结论:

  • 基于bootstrap的差异估计器为细分分层的传统方法提供了更好的替代方案.
  • 准确的差异估计对于可靠的调查结果至关重要,特别是在复杂的设计中.
  • 提出的方法提高了调查估计的精度和可靠性,在许多小层的情况.