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

Distance Corrections01:15

Distance Corrections

108
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
108
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

23.5K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
23.5K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

9.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
9.3K
Review and Preview01:10

Review and Preview

7.9K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
7.9K
Introduction to Statistics01:17

Introduction to Statistics

53.7K
The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
53.7K
Ratio Level of Measurement00:54

Ratio Level of Measurement

19.5K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
19.5K

You might also read

Related Articles

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

Sort by
Same author

DISTANCE DATA REVISITED.

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

THE PATTERN OF CLADISTICS.

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

HENNIG DEFINED PARAPHYLY.

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

PHENETICS IN CAMOUFLAGE.

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

THE RETENTION INDEX AND THE RESCALED CONSISTENCY INDEX.

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

A/The Brief History of Three-Taxon Analysis.

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

Phylogenetic systematics of the Homalonotidae (Trilobita): taxonomic reassessment and implications for Devonian trilobites.

Cladistics : the international journal of the Willi Hennig Society·2026
Same journal

Advancing FAIR phylogenetics for health threats: a systematic review of SARS-CoV-2 research and guidelines for future outbreaks.

Cladistics : the international journal of the Willi Hennig Society·2026
Same journal

Unravelling the phylogeny of armadillos and their kin (Mammalia, Xenarthra, Cingulata) combining morphological, molecular, and stratigraphic data.

Cladistics : the international journal of the Willi Hennig Society·2026
Same journal

Phylogenomics and the evolutionary history of the Oxyurida (pinworms).

Cladistics : the international journal of the Willi Hennig Society·2026
Same journal

Budding speciation, mitochondrial capture and introgression between surface and cave lineages in the Asellus aquaticus species complex.

Cladistics : the international journal of the Willi Hennig Society·2026
Same journal

Some considerations about Cotton and Wilkinson's "majority rule supertrees".

Cladistics : the international journal of the Willi Hennig Society·2026
See all related articles

Related Experiment Video

Updated: Oct 9, 2025

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction
10:41

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Published on: May 12, 2016

8.3K

DISTANCES AND STATISTICS.

James S Farris1

  • 1Department of Ecology and Evolution, State University of New York, Stony Brook NY 11794.

Cladistics : the International Journal of the Willi Hennig Society
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

Felsenstein

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Related Experiment Videos

Last Updated: Oct 9, 2025

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction
10:41

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Published on: May 12, 2016

8.3K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Felsenstein's work on sequence differences and phylogenetic tree construction is influential.
  • Additivity of distances is a key assumption in some phylogenetic methods.

Purpose of the Study:

  • To critically evaluate Felsenstein's claims regarding approximate additivity of sequence differences.
  • To assess the validity of Felsenstein's proposed nonadditive fitting method and nonnegativity restriction for branch lengths.
  • To analyze the assumptions and limitations of Felsenstein's significance test for phylogenetic data.

Main Methods:

  • Theoretical analysis of Felsenstein's models and methods.
  • Examination of the mathematical premises underlying additivity and distance assumptions.
  • Evaluation of the statistical properties of the proposed significance test.

Main Results:

  • Felsenstein's model for approximate additivity is shown to be unjustified.
  • The proposed nonadditive fitting method and nonnegativity restriction are based on flawed premises.
  • The significance test conflates sampling error with nonadditivity and relies on incorrect assumptions.

Conclusions:

  • Felsenstein's methods for phylogenetic analysis, particularly concerning distance additivity, are critically flawed.
  • The additive fitting program lacks essential features for handling ambiguous distance data.
  • Reevaluation of phylogenetic methods is needed, moving beyond unjustified assumptions of additivity.