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相关概念视频

Bonferroni Test01:10

Bonferroni Test

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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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...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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%...
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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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Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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相关实验视频

Updated: Jun 29, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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计算能力和样本大小用于多个应用中的错误发现率.

Yonghui Ni1, Anna Eames Seffernick1, Arzu Onar-Thomas1

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.

Genes
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新方法,使用p值组图近似来计算统计能力和样本大小,用于使用错误发现率 (FDR) 进行基因组分析. 一个R包,FDRsamplesize2,现在可用于更广泛的研究应用.

关键词:
错误发现率 错误发现率多次测试多次测试多次测试权力,权力,权力,权力.真正的零假设的比例.样本的大小 样本大小

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相关实验视频

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科学领域:

  • 基因组学就是基因组学.
  • 统计遗传学 统计遗传学
  • 生物信息学是一种生物信息学.

背景情况:

  • 错误发现率 (FDR) 是评估多重假设测试的基因组分析中的统计显著性的关键指标.
  • 充分的统计能力和适当的样本大小对于成功规划和执行此类研究至关重要.

研究的目的:

  • 为了获得一个新的公式来计算统计能力和样本大小,专门用于使用FDR进行分析.
  • 引入R包FDRsamplesize2,增强基因组研究中的功率和样本大小计算工具包.

主要方法:

  • 用一个p值直方图的三矩形近似来导出功率和样本大小公式.
  • 开发了R包FDRsamplesize2,集成了新的公式和现有的方法来进行全面的功率计算.

主要成果:

  • 在FDR控制的基因组分析中成功推导出计算统计功率和样本大小的新公式.
  • FDRsamplesize2 R包为各种研究设计提供了先进的功率计算功能,超出了当前软件限制.

结论:

  • 拟议的方法和FDRsamplesize2包为进行基因组数据分析的研究人员提供了宝贵的工具.
  • 这些进步在基因组学大数据时代促进了更强大的研究设计和可靠的统计推理.