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

Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.9K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.9K
Statistical Significance01:50

Statistical Significance

22.1K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.1K
Absolute Value Inequalities01:23

Absolute Value Inequalities

338
The absolute value is a mathematical tool that represents the distance of a number from zero on the number line, regardless of its sign. In the context of inequalities, absolute value expressions help define a range of permissible values or boundaries for a variable. These inequalities are commonly used in scientific modeling and data interpretation, where variability within or beyond a certain threshold must be captured precisely.An absolute value inequality of the form ∣x∣ ≤...
338
Mean Absolute Deviation01:13

Mean Absolute Deviation

3.4K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
3.4K
Quantifying Work02:30

Quantifying Work

24.5K
As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.
24.5K
Probability in Statistics01:14

Probability in Statistics

23.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.5K

You might also read

Related Articles

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

Sort by
Same author

Evolving roles of Data Coordinating Centers in multisite research: Challenges and adaptations from a rapid scoping review.

Journal of clinical and translational science·2026
Same author

Correction: Working to Increase Stability through Exercise (WISE): screening, recruitment, and baseline characteristics.

Trials·2026
Same author

Postanesthesia Apnea in Former Preterm and Term Infants: A Qualitative Systematic Review and Meta-analysis.

Anesthesiology·2026
Same author

Multivariate equivalence and component-wise superiority tests for paired samples.

Journal of biopharmaceutical statistics·2026
Same author

Semaglutide, weight loss, and cardiovascular outcomes in the SELECT trial.

Lancet (London, England)·2026
Same author

Hyperglycemia During Acute Pancreatitis and Progression to Early-Onset Diabetes After Recovery: Preliminary Findings From the Diabetes Related to Acute Pancreatitis and Its Mechanisms (DREAM) Study.

Diabetes care·2026

Related Experiment Video

Updated: Feb 8, 2026

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
11:19

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

15.5K

L-statistics of absolute differences for quantifying the agreement between two variables.

Elahe Tashakor1, Vernon M Chinchilli1

  • 1a Department of Public Health Sciences , Penn State College of Medicine , Hershey , PA , USA.

Journal of Biopharmaceutical Statistics
|June 29, 2018
PubMed
Summary

This study introduces a robust method for measuring agreement in clinical studies, improving upon the concordance correlation coefficient (CCC) for non-normally distributed data. The new approach enhances statistical performance for skewed or thick-tailed datasets.

Keywords:
-statisticsAgreementconcordance correlation coefficientrobust estimation

More Related Videos

Nasal Potential Difference to Quantify Trans-epithelial Ion Transport in Mice
08:55

Nasal Potential Difference to Quantify Trans-epithelial Ion Transport in Mice

Published on: July 4, 2018

8.2K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Related Experiment Videos

Last Updated: Feb 8, 2026

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts
11:19

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Published on: October 9, 2016

15.5K
Nasal Potential Difference to Quantify Trans-epithelial Ion Transport in Mice
08:55

Nasal Potential Difference to Quantify Trans-epithelial Ion Transport in Mice

Published on: July 4, 2018

8.2K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Clinical Research Methodology

Background:

  • Lin's concordance correlation coefficient (CCC) is widely used for continuous outcomes in clinical studies.
  • Standard CCC assumes normal data distribution, which is often violated in practice due to skewness or thick tails.
  • Existing robust methods have limitations, necessitating advanced approaches.

Purpose of the Study:

  • To propose a novel robust estimation approach for concordance correlation coefficient (CCC) indices.
  • To extend existing robust estimation methods by utilizing functionals for robust L-statistics.
  • To address the limitations of traditional CCC in non-normally distributed clinical data.

Main Methods:

  • Development of a new methodology focusing on functionals that yield robust L-statistics.
  • Application of the proposed approach to two real-world data examples.
  • Evaluation of statistical performance through comprehensive computer simulation studies.

Main Results:

  • The proposed method offers a robust alternative for calculating CCC.
  • Demonstrated effectiveness in handling skewed and thick-tailed data distributions.
  • Simulation studies indicate improved statistical performance compared to existing methods.

Conclusions:

  • The novel robust L-statistic approach provides a valuable tool for agreement assessment in clinical studies with non-ideal data.
  • This methodology enhances the reliability of agreement measures when normality assumptions are not met.
  • The findings support wider adoption of robust statistical techniques in clinical data analysis.