Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.1K
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...
9.1K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.5K
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...
3.5K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

10.0K
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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
10.0K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K
Genetics of Speciation02:16

Genetics of Speciation

23.4K
Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
23.4K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Monitoring polycyclic aromatic hydrocarbons deposition in eastern Canada using moss biomonitoring: A large-scale study of spatial patterns and environmental influences.

The Science of the total environment·2026
Same author

Key factors influencing Hg levels and trends in unperturbed oligotrophic temperate and boreal lakes.

Environmental pollution (Barking, Essex : 1987)·2024
Same author

Northeastern North America as a potential refugium for boreal forests in a warming climate.

Science (New York, N.Y.)·2016
Same author

Evolution of reduced post-copulatory molecular interactions in Drosophila populations lacking sperm competition.

Journal of evolutionary biology·2015
Same author

Response of canopy nitrogen uptake to a rapid decrease in bulk nitrate deposition in two eastern Canadian boreal forests.

Oecologia·2014
Same author

The effects of CO2 pressure and pH on the Suzuki coupling of basic nitrogen containing substrates.

Organic & biomolecular chemistry·2014

Related Experiment Video

Updated: Apr 10, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K

Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood.

D Houle1, K Meyer2

  • 1Department of Biological Science, Florida State University, Tallahassee, FL, USA.

Journal of Evolutionary Biology
|June 17, 2015
PubMed
Summary
This summary is machine-generated.

A new method, REML-MVN, efficiently estimates uncertainty in evolutionary parameters by resampling genetic variance-covariance matrices (G). It offers a computationally attractive alternative to bootstrap and MCMC methods for assessing confidence in evolutionary parameters.

Keywords:
G matrixevolutionevolvabilityquantitative geneticsrestricted maximum likelihoodsampling error

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.7K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.4K

Related Experiment Videos

Last Updated: Apr 10, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.7K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.4K

Area of Science:

  • Evolutionary biology
  • Quantitative genetics
  • Bioinformatics

Background:

  • Estimating uncertainty in evolutionary parameters is crucial for understanding evolutionary processes.
  • Traditional methods like bootstrapping and MCMC can be computationally intensive.
  • Additive genetic variance-covariance matrices (G) are key to studying evolutionary responses.

Purpose of the Study:

  • To introduce and evaluate a novel, computationally efficient method (REML-MVN) for estimating uncertainty in evolutionary parameters.
  • To compare the REML-MVN method with existing approaches such as the parametric bootstrap and Markov chain Monte Carlo (MCMC).
  • To assess the reliability of REML-MVN in estimating the variability of G matrix elements and evolvability statistics.

Main Methods:

  • Implementation of the REML-MVN method, which involves resampling G matrices from a multivariate normal distribution.
  • Application of REML-MVN, parametric bootstrap, and MCMC (MCMCglmm) to a 20-dimensional Drosophila wing dataset.
  • Comparison of sampling variances estimated by REML-MVN, parametric bootstrap, and MCMC.

Main Results:

  • REML-MVN, MCMC, and parametric bootstrap yielded similar estimates for sampling variances.
  • All tested methods slightly underestimated the error in well-estimated G matrix components.
  • REML analysis confirmed the full rank of the G matrix for the studied Drosophila population.

Conclusions:

  • REML-MVN is a computationally efficient and attractive alternative for assessing confidence in evolutionary parameters.
  • The method provides reliable estimates of uncertainty, comparable to more computationally demanding approaches.
  • This facilitates more accessible uncertainty estimation in evolutionary quantitative genetics.