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相关概念视频

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

190
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
190
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

177
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
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Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

122
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
122
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...
2.7K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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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:
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相关实验视频

Updated: Jun 28, 2025

A Within-Subject Experimental Design using an Object Location Task in Rats
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基于等级的多重对比测试的样本大小规划.

Anna Pöhlmann1, Edgar Brunner2, Frank Konietschke1

  • 1Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Biometrical journal. Biometrische Zeitschrift
|April 18, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了基于等级的多重对比测试的新样本大小规划方法. 这些准确的统计规划工具帮助研究人员确定检测治疗效应所需的样本大小.

关键词:
多重对比测试多重对比测试这是一个非参数的程序.权力考虑因素 权力考虑因素样本大小的确定样本大小的确定钢铁试验 钢铁试验 钢铁试验 钢铁试验

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科学领域:

  • 生物统计学 生物统计学
  • 统计方法 统计方法

背景情况:

  • 建立了排名方法来比较独立组.
  • 在基于等级的测试中确定样本大小的统计规划尚不发达.

研究的目的:

  • 在基于伪等级的多重对比测试中开发用于样本大小规划的数值算法.
  • 为基于等级的统计分析提供准确的样本大小估计.

主要方法:

  • 为样本大小估计开发数值算法.
  • 讨论治疗效应和差异参数近似值.
  • 配对对比全球排名方法的比较.

主要成果:

  • 开发了精确的样本大小估计器,用于基于伪等级的多重对比测试.
  • 模拟研究证实了拟议的样本大小方法的准确性.
  • 通过真实数据示例展示了实际应用.

结论:

  • 开发的方法为基于等级的统计测试中的样本规模规划提供了必要的工具.
  • 精确的样本大小测定提高了检测治疗效应的可靠性.
  • 这项研究弥合了基于等级的比较研究的统计规划上的差距.