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

Randomized Experiments01:13

Randomized Experiments

8.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.2K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

755
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
755
Random Variables01:09

Random Variables

14.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
14.9K
Group Design02:01

Group Design

9.8K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
9.8K
Random Sampling Method01:09

Random Sampling Method

12.8K
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. Data are the result of sampling from a 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. Among the various sampling methods used by...
12.8K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

345
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
345

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

DISTANCES AND STATISTICS.

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

Related Experiment Video

Updated: Oct 10, 2025

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

RASA Attributes Highly Significant Structure to Randomized Data.

James S Farris1

  • 1Molekylärsystematiska laboratoriet, Naturhistoriska riksmuseet, Box 50007, SE 104-105, Stockholm, Sweden.

Cladistics : the International Journal of the Willi Hennig Society
|December 16, 2021
PubMed
Summary
This summary is machine-generated.

Relative apparent synapomorphy analysis (RASA) is an unreliable method for detecting hierarchic structure. It often fails to identify true structure and incorrectly flags random data as significant, making it unsuitable for data analysis.

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.1K

Related Experiment Videos

Last Updated: Oct 10, 2025

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
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.1K

Area of Science:

  • Phylogenetics
  • Systematics
  • Computational Biology

Background:

  • Relative apparent synapomorphy analysis (RASA) was proposed as a statistical test for hierarchic structure in data.
  • The method aimed to use regression slopes between cladistic and phenetic similarity measures.

Purpose of the Study:

  • To critically evaluate the statistical validity and effectiveness of RASA for detecting hierarchic structure.
  • To identify the limitations and potential biases of the RASA method and its associated significance test.

Main Methods:

  • Analysis of the statistical underpinnings of RASA, including its use of similarity indices and regression analogy.
  • Evaluation of the original RASA significance test (tRASA) for Type I error rates.
  • Comparison of RASA's performance against a permutation test and the total support test using simulations and real datasets.
  • Investigation of RASA's sensitivity to deviations from a molecular clock.

Main Results:

  • RASA's similarity indices are both measures of phenetic similarity, not distinguishing cladistic and phenetic aspects.
  • The original tRASA significance test exhibits inflated Type I error rates, rejecting the null hypothesis too often.
  • RASA frequently misidentifies random data as structured and fails to detect significant structure in genuinely structured matrices.
  • RASA demonstrated poor performance in simulations and real-world datasets, correctly identifying structure in only 2 out of 13 cases.
  • The total support test significantly outperforms RASA in detecting hierarchic structure.
  • Deviations from a molecular clock further impair RASA's ability to detect structure.

Conclusions:

  • RASA is not a reliable method for assessing hierarchic structure in biological data.
  • The statistical basis of RASA is flawed, leading to inaccurate conclusions about data structure.
  • RASA should not be used for selecting characters or character codings in phylogenetic analyses.
  • Alternative methods, such as the total support test, are more effective and reliable for inferring hierarchic structure.