Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

You might also read

Related Articles

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

Sort by

Same author

DISTANCE METHODS: A REPLY TO FARRIS.

Cladistics : the international journal of the Willi Hennig Society·2021

Same author

CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP.

Evolution; international journal of organic evolution·2017

Same author

EVOLUTIONARY TREES FROM GENE FREQUENCIES AND QUANTITATIVE CHARACTERS: FINDING MAXIMUM LIKELIHOOD ESTIMATES.

Evolution; international journal of organic evolution·2017

Same author

SKEPTICISM TOWARDS SANTA ROSALIA, OR WHY ARE THERE SO FEW KINDS OF ANIMALS?

Evolution; international journal of organic evolution·2017

Same author

PHYLOGENIES FROM RESTRICTION SITES: A MAXIMUM-LIKELIHOOD APPROACH.

Evolution; international journal of organic evolution·2017

Same author

DISTANCE METHODS FOR INFERRING PHYLOGENIES: A JUSTIFICATION.

Evolution; international journal of organic evolution·2017

Same journal

Traffic Reduction during COVID-19 Lockdowns Benefited Species Already Tolerant of Noise Pollution: An Acoustic Analysis.

The American naturalist·2026

Same journal

On Pachycephalosaurs, Trade-Offs, and the Historical Genesis of Sociosexual Display Structures.

The American naturalist·2026

Same journal

Structured Landscapes Promote Persistence by Favoring Prudent Predators.

The American naturalist·2026

Same journal

Can Carbon Economy Explain Leaf Dynamic Seasonality in a Tropical Seasonal Rainforest?

The American naturalist·2026

Same journal

Behavior and Physiology Outpace Form When Linking Traits to Ecological Responses within Populations: A Meta-Analysis.

The American naturalist·2026

Same journal

Seminal Fluid Proteins as Regulation Factors for Optimizing Reproduction: A Modeling Approach.

The American naturalist·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 26, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

A comparative method for both discrete and continuous characters using the threshold model.

Joseph Felsenstein¹

¹Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA. joe@gs.washington.edu

The American Naturalist

|January 6, 2012

Summary

This summary is machine-generated.

Sewall Wright's threshold model analyzes discrete character evolution using unobserved liability. This method, enhanced with Markov chain Monte Carlo, integrates discrete and continuous traits for comparative analyses.

More Related Videos

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

Related Experiment Videos

Last Updated: May 26, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

Area of Science:

Evolutionary biology
Quantitative genetics
Phylogenetics

Background:

The threshold model, developed by Sewall Wright in 1934, provides a framework for modeling the evolution of discrete characters.
This model posits an underlying, unobserved quantitative trait (liability) that determines the discrete state based on a threshold.
Extending this model allows for the integration of both discrete and continuous characters in evolutionary studies.

Purpose of the Study:

To adapt and apply Sewall Wright's threshold model for inferring evolutionary covariances of liabilities for discrete characters.
To develop a comparative-methods analysis framework that can simultaneously accommodate both discrete and continuous characters.
To evaluate the model's performance and limitations through simulations.

Main Methods:

Utilizing a Markov chain Monte Carlo (MCMC) algorithm to sample liability values consistent with phylogenetic data and observed discrete character states.
Applying the MCMC approach to continuous characters by treating observed tip species values as the liability values.
Conducting simulations to assess the accuracy and precision of estimated liability covariances.

Main Results:

The MCMC algorithm successfully estimates the evolutionary covariances of liabilities for discrete characters.
The approach allows for a unified comparative-methods analysis combining discrete and continuous traits.
Simulation results demonstrate successful estimation of covariances, with precision contingent on species number.

Conclusions:

Sewall Wright's threshold model, implemented with MCMC, is effective for inferring evolutionary covariances and integrating discrete and continuous character analyses.
The model's flexibility allows for extensions to incorporate within-species variation and quantitative genetics models.
While effective, accurate covariance estimation requires substantial species data, and model applicability across diverse groups warrants consideration.