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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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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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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.
William S. Gosset (1876–1937) of the...
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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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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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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Comparing direct and indirect selfing rate estimates: when are population-structure estimates reliable?

A Bürkli1,2, N Sieber1,2, K Seppälä1

  • 1EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Department of Aquatic Ecology, Dübendorf, Switzerland.

Heredity
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Estimating selfing rates in hermaphrodites is challenging. The RMES method accurately estimated selfing in snails, unlike FIS, proving robust against null alleles and a cost-efficient alternative.

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Area of Science:

  • Evolutionary biology
  • Population genetics

Background:

  • Estimating selfing rates is crucial for understanding hermaphroditic species evolution.
  • Traditional methods like FIS and identity disequilibrium (e.g., RMES) have limitations.
  • Direct progeny arrays are data-intensive and often impractical for natural populations.

Purpose of the Study:

  • To compare direct progeny-array and indirect population-level methods for estimating selfing rates.
  • To assess the accuracy and robustness of FIS and RMES methods in a natural population.
  • To evaluate the impact of null alleles on selfing rate estimations.

Main Methods:

  • Utilized both direct progeny-array and indirect population-level methods.
  • Collected data from 1034 field-collected embryos from 60 families and 316 adults.
  • Employed 10 highly polymorphic microsatellites in the freshwater snail Radix balthica.

Main Results:

  • Progeny arrays detected no selfed embryos.
  • FIS-based estimates showed significant positive selfing rates across all samples.
  • RMES estimates closely matched progeny-array results and were unaffected by null alleles.

Conclusions:

  • FIS method produced biased estimates, particularly with null alleles.
  • RMES is a reliable, cost-efficient alternative to progeny arrays for estimating selfing rates.
  • RMES assumptions were met or irrelevant in this study population, validating its use.