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

Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.5K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.5K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
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).
8.9K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

7.1K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
7.1K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

661
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...
661
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.1K
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...
1.1K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

480
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
480

You might also read

Related Articles

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

Sort by
Same author

Evaluation of endophyte-mediated resistance against Noctua pronuba (Lepidoptera: Noctuidae) in cool-season grasses in Oregon.

Environmental entomology·2026
Same author

Implementing Project Optimus in Oncology Dosage Optimization: Where are We Now?

Therapeutic innovation & regulatory science·2026
Same author

Advancing Platform Trials in Early Oncology by Using MATS and EXNEX in Randomized Controlled Trials.

Therapeutic innovation & regulatory science·2026
Same author

Tropical Fruit Aroma in White Wines: Exploring the Role of Esters and Thiols in Chardonnay and Sauvignon Blanc Wines.

Journal of food science·2025
Same author

Ensuring Quality and Interpretability of Progression Free Survival and Overall Survival in Oncology Clinical Trials.

Therapeutic innovation & regulatory science·2025
Same author

Transcriptomic profiling of eyeless Drosophila reveals molecular network orchestrating response to blue light.

Journal of photochemistry and photobiology. B, Biology·2025

Related Experiment Video

Updated: Apr 16, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

43.1K

Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.

Gu Mi1, Yanming Di2, Daniel W Schafer1

  • 1Department of Statistics, Oregon State University, Corvallis, Oregon, United States of America.

Plos One
|March 20, 2015
PubMed
Summary

This study introduces new statistical tests and graphics to assess negative binomial (NB) regression models, crucial for RNA sequencing (RNA-Seq) data analysis. These tools help ensure model accuracy and improve the reliability of gene expression studies.

More Related Videos

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.6K

Related Experiment Videos

Last Updated: Apr 16, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

43.1K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.6K

Area of Science:

  • Bioinformatics and Computational Biology
  • Statistical Genetics
  • Genomics

Background:

  • Negative binomial (NB) regression is widely used for analyzing RNA sequencing (RNA-Seq) data due to its ability to model count data with overdispersion.
  • Assessing the adequacy of NB assumptions and the appropriateness of dispersion parameter models is critical for robust RNA-Seq analysis, especially given the typical high dimensionality and low sample size.
  • Existing methods for RNA-Seq analysis often employ power-saving strategies by modeling NB dispersion parameters, necessitating thorough investigation into their robustness and statistical power.

Purpose of the Study:

  • To develop and evaluate statistical tests and diagnostic graphics for assessing the adequacy of NB regression models.
  • To specifically address the assessment of NB assumption fit and the appropriateness of models for NB dispersion parameters in the context of RNA-Seq data.
  • To investigate the trade-offs between robustness and statistical power in NB regression models used for RNA-Seq analysis.

Main Methods:

  • Proposal of simulation-based statistical tests designed to evaluate model adequacy for NB regression.
  • Development of diagnostic graphics to visually assess the fit of NB models and their dispersion parameterizations.
  • Utilization of both simulated and real RNA-Seq data examples to demonstrate the effectiveness of the proposed methods.

Main Results:

  • The proposed simulation-based tests and diagnostic graphics are effective in detecting misspecification of the NB mean-variance relationship.
  • The methods successfully judge the adequacy of fit for various NB dispersion models commonly used in RNA-Seq analysis.
  • Demonstrated practical utility in assessing the reliability of NB regression models for gene expression studies.

Conclusions:

  • The developed tools provide essential capabilities for validating NB regression models in bioinformatics.
  • Accurate model assessment enhances the reliability and interpretability of findings from RNA-Seq experiments.
  • These methods contribute to more robust and powerful analyses in the rapidly growing field of RNA-Seq data interpretation.