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相关概念视频

Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

8.1K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
8.1K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.6K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.6K
Test for Homogeneity01:23

Test for Homogeneity

2.0K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.0K
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
Bonferroni Test01:10

Bonferroni Test

2.7K
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
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.2K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.2K

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相关实验视频

Updated: Jun 26, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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在调解途径分析中,针对复合零假设的自适应性启动测试.

Yinqiu He1, Peter X K Song2, Gongjun Xu3

  • 1Department of Statistics, University of Wisconsin, Madison, WI, USA.

Journal of the Royal Statistical Society. Series B, Statistical methodology
|May 15, 2024
PubMed
概括

我们开发了一个新的自适应引导框架,以改善调解分析. 这种方法通过解决复合零假设所带来的挑战,提高了测试中介效应 (ME) 的统计能力.

关键词:
这是一个bootstrap系统.复合假设是复合假设.调解分析 调解分析结构方程模型的结构方程模型.

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相关实验视频

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科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 流行病学 流行病学

背景情况:

  • 调解分析研究了风险如何通过中间变量影响结果.
  • 目前的调解效应 (ME) 测试面临着由于复合式零假设的挑战,导致保守和不足的测试.
  • 越来越需要跨科学学科进行强大的调解分析.

研究的目的:

  • 为调解途径分析开发一个自适应的引导式测试框架.
  • 增强统计能力,在复合零假设下提供I型错误控制.
  • 解决中介效应测试中现有方法的局限性.

主要方法:

  • 开发了一个自适应式引导测试框架.
  • 该框架在调解分析中容纳了各种复合零假设.
  • 适用于系数和联合显著性测试的乘积.

主要成果:

  • 拟议的适应性测试程序提供了改进的I型错误控制.
  • 该方法与现有的调解测试相比,显著提高了统计能力.
  • 理论特性和数值示例证明了框架的有效性.

结论:

  • 适应式引导框架为调解分析提供了强大的解决方案.
  • 这种方法提高了测试调解效应的可靠性和功率.
  • 该方法广泛适用于各种调解途径分析.