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

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
7.0K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

490
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
490
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.9K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
27.9K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.0K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
6.0K
Statistical Significance01:50

Statistical Significance

21.9K
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...
21.9K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

599
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
599

You might also read

Related Articles

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

Sort by
Same author

Statistics of twinning in strained ferroelastics.

Journal of physics. Condensed matter : an Institute of Physics journal·2017
Same author

Retraction Note to: Relationship between inflammatory cytokines and risk of depression, and effect of depression on the prognosis of high grade glioma patients.

Journal of neuro-oncology·2017
Same author

The Edge Stresses and Phase Transitions for Magnetic BN Zigzag Nanoribbons.

Scientific reports·2017
Same author

Photonics-based broadband radar for high-resolution and real-time inverse synthetic aperture imaging.

Optics express·2017
Same author

Selective malaria antibody screening among eligible blood donors in Jiangsu, China.

Revista do Instituto de Medicina Tropical de Sao Paulo·2017
Same author

Electron Beam Etching of CaO Crystals Observed Atom by Atom.

Nano letters·2017
Same journal

Commentary on clim-TIME: A paradigm shift in spatially resolved perturbation mapping of the metastatic tumor microenvironment.

Genes & diseases·2026
Same journal

A novel chimeric RNA RPGR-EEF1A1 enhances autophagy by interaction with the small GTPase RAB37 in a GTP-dependent manner.

Genes & diseases·2026
Same journal

Immunotherapy for sepsis: From single-cell scenarios to clinical translation.

Genes & diseases·2026
Same journal

<i>FLCN</i> c.1300G>A: Selective advantage in medieval France.

Genes & diseases·2026
Same journal

Loss of microbial signals reprograms endocrine microenvironments and consistently reduces RESISTIN expression in the adrenal and thyroid cells of germ-free pigs.

Genes & diseases·2026
Same journal

Rejuvenation of corticospinal neurons enhances rehabilitation-associated corticospinal tract axon sprouting and functional recovery post photothrombotic ischemic stroke in mice.

Genes & diseases·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

17.9K

Hypothesis Testing and Statistical Analysis of Microbiome.

Yinglin Xia1,2, Jun Sun2

  • 1Division of Academic Internal Medicine and Geriatrics, Department of Medicine University of Illinois at Chicago, Chicago, IL.

Genes & Diseases
|September 11, 2018
PubMed
Summary
This summary is machine-generated.

This review covers statistical and bioinformatics tools for analyzing gut microbiome data, emphasizing host-microbiome-environment interactions. It highlights current methods and future directions for statistical modeling in microbiome research.

Keywords:
IBDVitamin D receptorbioinformaticsbiostatisticscancerdietdysbiosishypothesis testinginflammationmicrobiomeobesitystatistical methods and models

More Related Videos

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome
08:38

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome

Published on: March 31, 2023

1.2K
Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes
07:42

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Published on: January 10, 2022

4.8K

Related Experiment Videos

Last Updated: Feb 5, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

17.9K
Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome
08:38

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome

Published on: March 31, 2023

1.2K
Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes
07:42

Quantification of the Potential Impact of Glyphosate-Based Products on Microbiomes

Published on: January 10, 2022

4.8K

Area of Science:

  • Microbiology
  • Biostatistics
  • Bioinformatics

Background:

  • The Human Microbiome Project (2008) spurred the development of numerous analytical tools for microbiome studies.
  • Understanding the gut microbiome requires analyzing complex interactions between the host, microbes, and environment.

Purpose of the Study:

  • To review statistical and bioinformatics tools used in gut microbiome research.
  • To discuss research and statistical hypotheses, focusing on mechanistic concepts.
  • To explore future directions in statistical method development for microbiome studies.

Main Methods:

  • Review of existing biostatistical and bioinformatic tools.
  • Analysis of statistical hypotheses and mechanistic concepts in gut microbiome research.
  • Highlighting recent advancements in statistical methods and models.

Main Results:

  • Numerous statistical and computational tools are available for microbiome data analysis.
  • Mechanistic concepts are crucial for understanding host-microbiome-environment relationships.
  • Progress has been made in developing new statistical methods and models.

Conclusions:

  • Statistical and computational tools are essential for advancing microbiome research.
  • Further development of statistical methods is needed to address current limitations.
  • Future research should focus on sophisticated models for complex microbiome interactions.