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相关概念视频

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.1K
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...
8.1K
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
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.8K
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 +...
8.8K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.6K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.6K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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相关实验视频

Updated: Jul 22, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

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将池级不确定性纳入人口统计推理中

João Carvalho1, Hernán E Morales2, Rui Faria3,4

  • 1cE3c - Centre for Ecology, Evolution and Environmental Changes & CHANGE - Global Change and Sustainability Institute, Departamento de Biologia Animal, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Portugal.

Molecular ecology resources
|July 21, 2023
PubMed
概括

我们开发了一种新的近似贝叶斯计算 (ABC) 方法,使用聚合序列 (Pool-seq) 数据重建进化历史. 这种方法解释了Pool-seq错误,使得可靠的人口推断和理解适应.

关键词:
游泳池-seqqq 的时间一个R包一个R包大致的贝叶斯计算.人口统计推断推断的人口统计推断.生态型的形成 生态型的形成

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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相关实验视频

Last Updated: Jul 22, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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科学领域:

  • 人口遗传学 人口遗传学
  • 进化生物学是进化的生物学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 聚合样本的下一代测序 (Pool-seq) 广泛用于人口多样性分析.
  • 由于个人贡献不平等等因素,Pool-seq数据可能会很,这限制了其在进化历史重建中的使用.
  • 现有的方法往往不能充分解决Pool-seq的特定错误源.

研究的目的:

  • 开发一种新的近似贝叶斯计算 (ABC) 方法,从Pool-seq数据推断人口历史.
  • 为了明确地建模和考虑Pool-seq数据中固有的噪声源.
  • 评估Pool-seq数据在区分生态型形成的不同场景和推断人口参数方面的有用性.

主要方法:

  • 开发了一种新的近似贝叶斯计算 (ABC) 方法,结合了Pool-seq错误模型.
  • 共同建模的Pool-seq数据,人口史和选择效应.
  • 采用了使用局部和相对统计数据/参数子集的计算效率高的模拟.

主要成果:

  • 该ABC方法成功地从Pool-seq数据中推断出人口历史参数,并考虑技术错误.
  • 模拟研究证实了Pool-seq数据能够区分生态型形成场景 (单个与并行起源).
  • 适用于Littorina saxatilis的数据表明生态型差异早于本地殖民化,尽管基因流动仍存在.

结论:

  • 使用 Pool-seq 数据与拟议的 ABC 方法进行人口建模和推断是可行的和可靠的.
  • 该方法为了解自然种群适应的遗传基础提供了有价值的工具.
  • 这项工作促进了对分析人口基因组学数据的零模型的开发,特别是来自聚合样本的数据.