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Related Concept Videos

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Confidence Coefficient

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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...
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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.
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A complete procedure for testing a claim about a population proportion is provided here.
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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.
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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.
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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...
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Quantifying generalized trust in individuals and counties using language.

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
Summary
This summary is machine-generated.

Generalized trust can be measured using language analysis from social media. Higher trust correlates with positive language and better community health and satisfaction.

Keywords:
data driven approachesgeneralized trustlanguage analysissocial epidemiologysocial media

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Area of Science:

  • Social Psychology
  • Computational Social Science
  • Digital Humanities

Background:

  • Trust is crucial for societal functions like civic cooperation and economic growth.
  • Declining trust in institutions and increasing partisan division in the U.S. necessitate new measurement methods.
  • Existing trust assessments can be obtrusive or difficult to scale.

Purpose of the Study:

  • To develop and validate a language-based assessment for measuring generalized trust.
  • To apply this assessment to large-scale social media data to estimate trust levels across U.S. counties.
  • To explore the relationship between estimated trust levels and community well-being.

Main Methods:

  • Collected language data from over 16,000 Facebook users and correlated it with self-reported trust scores.
  • Developed a language-based trust assessment model.
  • Applied the model to over 1.6 billion geotagged tweets (2009-2015) to estimate trust in 2,041 U.S. counties.

Main Results:

  • Generalized trust was associated with the use of more affiliative words (e.g., "love", "we", "friends") and fewer angry words (e.g., "hate", "stupid").
  • Social word associations were weak, primarily driven by negative associations with "othering" terms.
  • Higher trust counties showed better physical health and higher life satisfaction, according to CDC and Gallup data.

Conclusions:

  • Language analysis offers a scalable, low-cost, and unobtrusive method for assessing generalized trust.
  • Estimated trust levels correlate with measurable community health and well-being indicators.
  • This approach can help monitor population-level trust dynamics.