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Related Concept Videos

Distributions to Estimate Population Parameter01:26

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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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.
William S. Gosset (1876–1937) of the...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
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Inferring within-host bottleneck size: A Bayesian approach.

R Dybowski1, O Restif1, D J Price1

  • 1Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, CB3 0ES, UK.

Journal of Theoretical Biology
|September 19, 2017
PubMed
Summary
This summary is machine-generated.

Understanding bacterial infection dynamics in animals is crucial. This study compares methods for estimating bottleneck size during invasive bacterial infections, offering a flowchart to guide analysis of experimental data.

Keywords:
Bayesian inferenceBottlenecksSalmonellaWildtype isogenic tagged strains (WITS)

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Area of Science:

  • Microbiology
  • Infectious Disease Dynamics
  • Quantitative Biology

Background:

  • Recent advancements in microbiology reveal the significance of stochastic bottlenecks in bacterial infections within laboratory animals.
  • These bottlenecks are critical at the onset of invasive disease, influencing infection progression.

Purpose of the Study:

  • To compare existing and introduce a new sampling approach for bottleneck-size estimation in within-host bacterial infections.
  • To provide guidance on selecting appropriate methods based on experimental conditions and data availability.

Main Methods:

  • Bayesian comparison of bottleneck-size estimation approaches.
  • Evaluation based on the availability of isogenic multitype bacteria (e.g., WITS), knowledge of post-bottleneck dynamics, and suitability of dilution with monotype bacteria.
  • Introduction of a novel sampling approach for bottleneck-size estimation.

Main Results:

  • Not all bottleneck-size estimation methods are equally suitable for all experimental scenarios.
  • The study outlines specific criteria for method selection, including data type and bacterial strain availability.
  • A guiding flowchart is presented to aid researchers in choosing the most appropriate quantitative models.

Conclusions:

  • Quantitative models are essential for refining the analysis of animal experiment data in microbiology.
  • The developed flowchart aims to promote the use of robust methods for bottleneck-size estimation.
  • Improved estimation of bacterial bottleneck sizes can enhance our understanding of infection dynamics.