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Related Concept Videos

Uncertainty: Confidence Intervals00:54

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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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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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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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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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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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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Estimating uncertainty and reliability of social network data using Bayesian inference.

Damien R Farine1, Ariana Strandburg-Peshkin2

  • 1Edward Grey Institute of Field Ornithology, Department of Zoology , University of Oxford , Oxford, UK ; Department of Anthropology , University of California Davis , Davis, CA, USA ; Smithsonian Tropical Research Institute , Ancon, Panama.

Royal Society Open Science
|October 17, 2015
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Bayesian inference helps estimate uncertainty in animal social network analysis, especially with limited data. This method provides reliable estimates for social structures when sampling is sparse.

Keywords:
Bayesian inferencegroup-livinginteractionssocial network analysissocial structureuncertainty

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Area of Science:

  • Behavioral Ecology
  • Network Science
  • Quantitative Biology

Background:

  • Social network analysis is increasingly used to study animal societies.
  • Limited sample sizes in animal social network studies can lead to uncertainty in interaction rates.

Purpose of the Study:

  • To present a Bayesian inference method for incorporating uncertainty into animal social network analyses.
  • To test the reliability of this method against bootstrapping for simulated networks.
  • To propose a method for estimating the reliability of observed animal social networks.

Main Methods:

  • Bayesian inference for network analysis.
  • Simulation of animal social networks.
  • Comparison with bootstrapping methods.

Main Results:

  • Bayesian inference effectively captures local and global network properties.
  • The method provides realistic uncertainty estimates for edge weights, particularly with sparse sampling.
  • Observed networks closely approximate true social structures with sufficient sampling.

Conclusions:

  • Bayesian inference is a valuable tool for quantifying uncertainty in animal social network analysis.
  • The proposed methods enhance the reliability and interpretability of animal social network data.
  • Simple procedures can significantly improve the estimation of uncertainty and reliability in this field.