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Bonferroni Test01:10

Bonferroni Test

3.5K
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...
3.5K
Behrens–Fisher Test00:57

Behrens–Fisher Test

319
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
319
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.9K
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:
6.9K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

6.4K
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;...
6.4K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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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...
4.3K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
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...
4.0K

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相关实验视频

Updated: Mar 18, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

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使用贝叶斯因子测试组平均差异的最佳组大小.

Mirjam Moerbeek1

  • 1Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands.

Journal of applied statistics
|March 16, 2026
PubMed
概括
此摘要是机器生成的。

确定研究的最佳组大小至关重要. 这项研究引入了贝叶斯方法来最大限度地提高贝叶斯因子,为异质研究设计提供了比传统方法更好的替代方案.

关键词:
分析差异的分析.贝叶斯因子是一个贝叶斯因子.预算上的限制.信息性假设是一个信息性的假设.最佳设计方法的最佳设计方法

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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
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Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

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相关实验视频

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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
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科学领域:

  • 生物统计学 生物统计学
  • 统计研究设计 统计研究设计

背景情况:

  • 确定适当的组大小对于比较平均结果的研究至关重要.
  • 基于零假设显著性测试的传统最佳设计方法具有局限性,特别是在异质成本或差异的情况下.
  • 贝叶斯假设测试提供了一个使用贝叶斯因子的替代框架.

研究的目的:

  • 确定最大化贝叶斯因子的最佳组大小,以比较跨组的平均结果.
  • 研究差异,成本和群体平均值对这些最佳群体大小的影响.
  • 为了比较贝叶斯最优设计的效率与传统方法和同等组分配.

主要方法:

  • 在贝叶斯假设测试框架内利用贝叶斯因子.
  • 通过最大化贝叶斯因子来计算最佳群体规模.
  • 通过比较得出的最佳组大小与传统最佳设计和相同组分配的最佳组大小.

主要成果:

  • 最佳的群体规模取决于差异,成本和群体平均值.
  • 传统的最佳设计和相同的组大小导致贝叶斯因子相比贝叶斯最佳设计更小.
  • 该方法用疼痛管理和喘研究的例子来说明方法.

结论:

  • 贝叶斯方法提供了一种可靠的方法来确定最佳组大小,特别是在具有异质参数的研究中.
  • 这种方法比传统方法提供了更有效的设计,从而为假设提供了更强有力的证据.
  • 有一个实用的工具 (Shiny app) 可用于实现这种最佳设计方法.