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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Confidence Intervals01:21

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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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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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The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
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Confidence intervals in molecular dating by maximum likelihood.

Emmanuel Paradis1, Santiago Claramunt2, Joseph Brown2

  • 1ISEM, Univ Montpellier, CNRS, IRD, Montpellier, France.

Molecular Phylogenetics and Evolution
|October 28, 2022
PubMed
Summary
This summary is machine-generated.

This study evaluates three bootstrap methods for molecular dating confidence intervals. While effective under strict clock models, performance varied with relaxed clock models and data characteristics.

Keywords:
BootstrapFelidaePenalized likelihoodPhylogenetics

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Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Molecular evolution

Background:

  • Molecular dating is crucial for estimating evolutionary event timings using molecular sequences.
  • Inferring confidence intervals for these dates is essential for robust evolutionary inference.
  • Penalized likelihood frameworks offer a method for phylogenetic analysis.

Purpose of the Study:

  • To assess the performance of three bootstrap methods for confidence intervals in molecular dating.
  • To evaluate the impact of different evolutionary models (strict vs. relaxed clocks) on method performance.
  • To identify factors influencing the accuracy and reliability of molecular dating confidence intervals.

Main Methods:

  • Nonparametric (direct tree bootstrapping), semiparametric (rate smoothing), and parametric (Poisson simulation) bootstrap methods were implemented.
  • Data pseudoreplicates were used to infer uncertainty in phylogenetic branch lengths.
  • Simulations were conducted under strict, uncorrelated, and correlated relaxed clock models of molecular evolution.

Main Results:

  • All three bootstrap methods performed well under a strict clock model.
  • Performance decreased with uncorrelated and correlated relaxed clock models.
  • Increasing calibration points, sequence length, and number of sequences generally improved confidence interval performance, depending on the model.

Conclusions:

  • Bootstrap methods for molecular dating confidence intervals are reliable under strict clock models.
  • Careful consideration of the evolutionary model is necessary when applying these methods, especially with relaxed clocks.
  • The number of calibration points and data size are key factors influencing the precision of molecular dating estimates.