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相关概念视频

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...
3.9K
Bonferroni Test01:10

Bonferroni Test

2.8K
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...
2.8K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
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:
5.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
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...
3.4K
McNemar's Test01:23

McNemar's Test

298
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
298

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相关实验视频

Updated: Jul 17, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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多重对比测试的样本大小规划

Anna Pöhlmann1, Frank Konietschke1

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

Biometrical journal. Biometrische Zeitschrift
|September 5, 2023
PubMed
概括
此摘要是机器生成的。

本研究为使用多重对比测试的多样样样本临床前研究引入了新的样本大小计算方法. 这些准确,易于使用的工具改善了与多个样本的临床试验的规划.

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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相关实验视频

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A Within-Subject Experimental Design using an Object Location Task in Rats
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科学领域:

  • 生物统计学 生物统计学
  • 临床前研究 临床前研究
  • 临床试验设计 临床试验设计

背景情况:

  • 已建立的样本大小计算存在于临床研究中的两个独立样本.
  • 在临床前研究中常见的多个样本的样本大小规划存在挑战.
  • 现有的方法通常使用难以解释效应大小的方差分析 (ANOVA).

研究的目的:

  • 开发和验证在临床前研究中用于多个样本的样本大小计算方法.
  • 采用多重对比测试程序,以便更易于解释的样本大小规划.
  • 提供准确和易于使用的工具,用于多个样本的临床试验设计.

主要方法:

  • 使用多重对比测试程序进行样本大小计算.
  • 使用钢型试验对参数 (正常性假设) 和非参数设计应用方法.
  • 由于未知分布和缺乏闭式公式,使用了近似解决方案和数值近似.

主要成果:

  • 为多个样本开发了准确的样本大小计算方法.
  • 模拟研究证实,这些方法实现了检测替代品的目标功率.
  • 建议的程序是有效的规划前临床和临床试验,多个样本.

结论:

  • 开发的基于多重对比测试的样本大小计算是准确和可靠的.
  • 这些方法为研究人员提供了一种有价值的工具,他们计划使用多个样本进行临床前和临床试验.
  • 这些程序可以通过公开可用的软件访问,从而促进它们的广泛采用.