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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

778
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
778
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

12.3K
The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
12.3K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

3.1K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
3.1K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

3.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
3.5K

You might also read

Related Articles

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

Sort by
Same author

Manipulating vector transmission reveals local processes in <i>Bartonella</i> communities of bats.

Parasitology·2026
Same author

Stochastic modeling of bovine tuberculosis dynamics in white-tailed deer.

Research in veterinary science·2025
Same author

Potential benefits of adaptive control strategies are outweighed by costs of infrequent, but dramatically larger disease outbreaks.

Royal Society open science·2025
Same author

United States cattle market location and annual market sales estimate data.

Data in brief·2025
Same author

Ectoparasite and bacterial population genetics and community structure indicate extent of bat movement across an island chain.

Parasitology·2024
Same author

Phylogenomics and the rise of the angiosperms.

Nature·2024

Related Experiment Video

Updated: Oct 10, 2025

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.0K

The relative sensitivity of different alignment methods and character codings in sensitivity analysis.

Mark P Simmons1, Kai F Müller2, Colleen T Webb1

  • 1Department of Biology, Colorado State University, Fort Collins, CO 80523, USA.

Cladistics : the International Journal of the Willi Hennig Society
|December 11, 2021
PubMed
Summary

Sensitivity analysis assesses phylogenetic clade robustness to alignment parameter changes. Direct optimization (POY) showed higher sensitivity than simultaneous (DCA) or progressive pairwise (MUSCLE) alignment methods.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Related Experiment Videos

Last Updated: Oct 10, 2025

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.0K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Area of Science:

  • Phylogenetics
  • Bioinformatics
  • Computational Biology

Background:

  • Phylogenetic analyses rely on accurate multiple sequence alignments.
  • Sensitivity analysis is crucial for evaluating the robustness of phylogenetic results to variations in alignment parameters.
  • Comparing different alignment algorithms is essential for understanding their impact on phylogenetic sensitivity.

Discussion:

  • This study investigated the sensitivity of phylogenetic analyses to alignment parameter variations across three methods: MUSCLE, DCA, and POY.
  • The research utilized both empirical (rDNA) and simulated datasets with varied transition, transversion, and gap costs.
  • Phylogenetic tree searches were conducted using diverse character-coding and weighting strategies.

Key Insights:

  • POY demonstrated equal or greater sensitivity to alignment parameter changes compared to DCA and MUSCLE on empirical datasets.
  • For random sequences, POY was more sensitive than MUSCLE, which was more sensitive than DCA, based on averaged jackknife values.
  • Equally weighted parsimony showed less sensitivity than using alignment cost functions, especially when treating gaps as missing data.

Outlook:

  • Further research could explore the application of these sensitivity analyses to larger and more complex biological datasets.
  • Investigating the computational efficiency of these alignment methods in conjunction with sensitivity analysis is warranted.
  • Developing standardized protocols for sensitivity analysis in phylogenetics could enhance the reliability of evolutionary reconstructions.