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相关概念视频

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

170
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
170
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.2K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.6K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.6K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

3.4K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
3.4K

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相关实验视频

Updated: May 29, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

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在大规模测试中最佳控制定向错误发现率.

Guozhu Tang1, Yicheng Kang2, Dongdong Xiang1

  • 1KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China.

Statistics in medicine
|February 6, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的三组模型来分析基因表达数据,改善过度表达和表达不足的基因的识别,同时控制错误的发现.

关键词:
一个单调的概率比率.多个测试多个测试测试.信号的分类信号的分类.三组模型中的三组模型.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Last Updated: May 29, 2025

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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科学领域:

  • 生物医学数据分析
  • 基因组学就是基因组学.
  • 统计遗传学 统计遗传学

背景情况:

  • 高通量技术同时测量数千个基因表达水平.
  • 识别过度表达和不足表达的基因对于分析基因表达数据至关重要.
  • 现有的两组模型无法控制过度和不足表达的基因的特定错误发现率.

研究的目的:

  • 为基因表达数据分析提出一个一般的三组模型.
  • 制定一个决策规则,控制表达过高和表达不足的虚假发现率.
  • 为了优化真实发现的预期数量,同时保持所需的虚假发现比例.

主要方法:

  • 开发一个一般的三组模型,适应测试统计数据之间的依赖性.
  • 一个单调结构的决策规则的设计,用于控制错误发现率.
  • 使用单调结构对双向虚假发现率约束的线性化.

主要成果:

  • 拟议的决策规则优化了真实发现,同时控制了对过度表达和不足表达的错误发现率.
  • 建议采用数据驱动的程序版本,并确定它们的一致性.
  • 新的程序在与现有方法的比较和基因组应用中表现出强的性能.

结论:

  • 拟议的三组模型和决策规则为基因表达分析中的错误发现提供了更好的控制.
  • 这种方法提高了识别差异表达基因的可靠性.
  • 这些发现对基因组研究和生物医学研究有重大影响.