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相关概念视频

Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

8.2K
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...
8.2K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.4K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
26.4K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

1.9K
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...
1.9K
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
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
What is a Hypothesis?01:14

What is a Hypothesis?

11.0K
A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
11.0K

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相关实验视频

Updated: Jul 5, 2025

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
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Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

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在线测试多重假设测试

David S Robertson1, James M S Wason2, Aaditya Ramdas3

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistical science : a review journal of the Institute of Mathematical Statistics
|January 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究回顾了在线假设测试中控制错误发现率 (FDR) 的方法,其中数据顺序到达. 它提供了一个全面的理论指南,应用程序和算法,用于管理实时数据流中的统计错误.

关键词:
进行A/B测试.数据存储库数据存储库.平台试验 平台试验第一种类型的错误率.

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科学领域:

  • 统计 统计 统计 统计
  • 数据科学数据科学数据科学
  • 生物信息学是一种生物信息学.

背景情况:

  • 大规模的假设测试在现代数据分析中很常见.
  • 控制错误发现率 (FDR) 对于可靠的结果至关重要.
  • 传统的FDR方法假设所有数据都同时可用,不适合在线,连续测试.

研究的目的:

  • 为在多重假设测试中进行在线错误率控制的方法提供全面的审查.
  • 为了弥合传统的线下FDR控制和顺序数据分析的需求之间的差距.
  • 为在线假设测试提供理论基础,实际应用和算法比较的见解.

主要方法:

  • 在过去15年中开发的在线错误率控制方法的文献综述.
  • 在线多重假设测试中的关键理论概念的阐述.
  • 模拟研究比较不同在线测试算法的性能.

主要成果:

  • 识别和综合了在线FDR控制方法的关键进展.
  • 通过应用示例展示了在线测试框架的实际适用性.
  • 提供了各种在线假设测试算法的比较性能分析.

结论:

  • 在线错误率控制是顺序数据分析的关键和不断发展的领域.
  • 审查的方法为管理实时假设测试中的统计错误提供了有效的策略.
  • 这项工作是研究人员和从业人员应用这些先进的统计技术的宝贵资源.