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相关概念视频

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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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.
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Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.4K
Significance Testing: Overview01:04

Significance Testing: Overview

11.5K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

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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)...
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Updated: Jan 15, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
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使用JASP中的BFpack模块对相关系数进行贝叶斯假设测试的教程.

Joris Mulder1, Julius Pfadt2, Eric-Jan Wagenmakers2

  • 1Department of Methodology and Statistics, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, the Netherlands. j.mulder3@tilburguniversity.edu.

Behavior research methods
|October 14, 2025
PubMed
概括
此摘要是机器生成的。

本教程介绍了使用JASP的BFpack模块对相关系数进行贝叶斯假设测试. 它为分析变量之间的关联提供了古典p值的灵活替代方案.

关键词:
贝叶斯因子是贝叶斯因子的一个因素.相对应系数 相对应系数假设测试 测试 假设测试后来的概率是后来的概率.

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科学领域:

  • 统计 统计 统计 统计
  • 心理测量 心理测量 心理测量
  • 量化心理学 量化心理学

背景情况:

  • 相关系数对于量化科学研究中变量之间的线性关联至关重要.
  • 使用p值对相关性的经典假设测试具有已知的局限性和对替代方案的有限软件支持.
  • 统计软件的有限可用性阻碍了对相关系数采用贝叶斯式测试程序.

研究的目的:

  • 展示如何使用JASP中的BFpack模块对各种相关系数进行贝叶斯假设测试.
  • 为研究人员提供一个用户友好的,开源的工具,用于先进的相关性分析,克服经典方法的局限性.

主要方法:

  • 在JASP软件中使用BFpack模块进行贝叶斯假设测试.
  • 展示了对产品时刻,多序列和四度相对关系的测试,包括部分相对关系.
  • 证明了贝叶斯对零相关性的测试以及依赖性和独立性相关性之间的比较.

主要成果:

  • 在JASP中的BFpack模块使灵活的贝叶斯假设测试用于各种相关性类型.
  • 该教程成功地说明了贝叶斯方法的应用,作为经典p值方法的替代方案.
  • 这项研究强调了通过免费开源软件实现先进的贝叶斯相关性测试的可访问性.

结论:

  • 在JASP中的BFpack模块为对应系数的贝叶斯假设测试提供了一个强大的和可访问的解决方案.
  • 这种方法为研究人员提供了强大的传统方法的替代方案,减轻了已知的局限性.
  • 该教程促进了贝叶斯统计方法在跨学科的相关性分析中的更广泛采用.