Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Confidence Coefficient01:24

Confidence Coefficient

10.3K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.3K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

9.2K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
9.2K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.9K
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...
3.9K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.7K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.7K
Confidence Intervals01:21

Confidence Intervals

10.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

10.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
10.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Bronchoscopic evaluation of human airway surface liquid pH.

American journal of respiratory and critical care medicine·2026
Same author

Language markers of student beliefs signal college success.

Communications psychology·2026
Same author

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century.

PloS one·2026
Same author

Balancing safety and access in Oregon's psilocybin services.

The International journal on drug policy·2026
Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026
Same author

A computational model of reward learning and habits on social media.

Nature communications·2026
Same journal

Cultural variation in age perceptions and developmental transitions.

Frontiers in social psychology·2026
查看所有相关文章

相关实验视频

Updated: Jan 12, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.5K

量化对使用语言的个人和县的普遍信任.

Salvatore Giorgi1, Jason Jeffrey Jones2, Anneke Buffone3

  • 1Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States.

Frontiers in social psychology
|November 7, 2025
PubMed
概括
此摘要是机器生成的。

一般化的信任可以通过社交媒体的语言分析来衡量. 更高的信任与积极的语言以及更好的社区健康和满意度相关.

关键词:
数据驱动的方法数据驱动的方法一般化的信任信任.语言分析语言分析.社会流行病学社会流行病学社交媒体 社交媒体

更多相关视频

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
06:42

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Published on: September 28, 2018

12.1K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

11.3K

相关实验视频

Last Updated: Jan 12, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.5K
Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
06:42

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Published on: September 28, 2018

12.1K
The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
05:15

The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

11.3K

科学领域:

  • 社会心理学 社会心理学
  • 计算社会科学 计算社会科学
  • 数字人文学科 数字人文学科

背景情况:

  • 信任对于公民合作和经济增长等社会功能至关重要.
  • 美国对机构的信任下降和党派分裂的增加,需要新的测量方法.
  • 现有的信任评估可能会很麻烦或难以扩展.

研究的目的:

  • 开发和验证一种基于语言的评估来衡量普遍的信任.
  • 将此评估应用于大规模的社交媒体数据,以估计美国各县的信任水平.
  • 探索估计的信任水平与社区福祉之间的关系.

主要方法:

  • 收集了超过16000名Facebook用户的语言数据,并将其与自我报告的信任分数相关联.
  • 开发了一个基于语言的信任评估模型.
  • 将该模型应用于超过16亿个地理标签的推特 (2009-2015年) 中,以估计美国2041个县的信任.

主要成果:

  • 一般化的信任与使用更多的关联词 (例如",爱"",我们"",朋友") 和更少的愤怒词 (例如",仇恨"",愚蠢") 相关.
  • 社会词汇关联较弱,主要是由与"其他"术语的负面关联驱动的.
  • 根据CDC和盖洛普的数据,更高的信任度县显示出更好的身体健康和更高的生活满意度.

结论:

  • 语言分析提供了一个可扩展,低成本和不引人注目的方法来评估通用信任.
  • 估计的信任水平与可测量的社区健康和福祉指标相关联.
  • 这种方法可以帮助监测人口层面的信任动态.