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相关概念视频

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...
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Statistical Significance01:50

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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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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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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Fineness Modulus01:19

Fineness Modulus

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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
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Behrens–Fisher Test00:57

Behrens–Fisher Test

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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...
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Updated: Jun 28, 2025

Determining the Mechanical Strength of Ultra-Fine-Grained Metals
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细粒度效果大小的细粒度效果大小.

John M Ferron1, Megan S Kirby1, Lodi Lipien1

  • 1Department of Educational and Psychological Studies, University of South Florida.

School psychology (Washington, D.C.)
|April 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新方法,用于在单个实验设计中估计和绘制细粒度效应大小的图形. 这种方法提供了对随着时间的推移对干预措施的个体反应的更详细的见解,特别是在自闭症学生中.

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科学领域:

  • 行为科学 行为科学
  • 教育心理学教育心理学
  • 干预研究研究干预研究

背景情况:

  • 对单个案例实验设计 (SCED) 的系统审查往往缺乏详细的个人反应数据.
  • 现有的效果大小测量可以掩盖微妙的,特定于时间和特定于案例的干预影响.
  • 准确的测量对于了解特殊教育干预效果至关重要.

研究的目的:

  • 开发和演示一种方法来估计和绘制SCED中细粒度效应大小的图形.
  • 为了提供一个更透明的视图的个人对干预的反应随着时间的推移.
  • 加强对自闭症学生自我管理干预措施的分析.

主要方法:

  • 开发了一种用于估计细粒度效果大小的新方法,考虑具体情况和时间的变化.
  • 根据三个不同的基线稳定性假设证明了该方法:结果,水平和趋势稳定性.
  • 应用该方法来绘制来自三项关于自我管理干预的SCED研究的个人效果轨迹图.

主要成果:

  • 细粒度效果大小方法提供了细微的,个人级别的干预反应数据.
  • 可视化个体效应轨迹揭示了随时间变化的特定模式.
  • 该方法提高了SCED研究中干预效应的解释性.

结论:

  • 细粒度效果大小为SCED中的干预效应提供了更透明和更详细的理解.
  • 开发的方法对于分析自闭症学生的自我管理干预是有价值的.
  • 需要进一步的研究来解决局限性,并扩大这种图形技术的应用.