Jove
Visualize
Contact Us

Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

463
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,...
463
Statistical Significance01:50

Statistical Significance

21.2K
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...
21.2K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

15.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
15.3K
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

881
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
881
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

380
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
380
What are Populations and Communities?00:30

What are Populations and Communities?

37.4K
Overview
37.4K

You might also read

Related Articles

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

Sort by
Same author

MeLSI: Metric Learning for Statistical Inference in microbiome community composition analysis.

mSystems·2026
Same author

Bacterial Communities Identified by Standard Culture and 16S rRNA Amplicon Sequencing in Ophthalmic Preservative-Free Dexamethasone SP Used in Canine Cataract Surgery.

Veterinary ophthalmology·2026
Same author

Stroke-induced gut microbiome dysbiosis accelerates Alzheimer's disease progression.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Eco-tank Housing Maintains Wild-Type Microbiota and Rewilds the Laboratory Mouse Gut Microbiome to Restore Natural Immune Tone.

bioRxiv : the preprint server for biology·2026
Same author

Exploring the Interplay Between Fatigue and the Oral Microbiome: A Longitudinal Approach.

Microorganisms·2025
Same author

Biological and technical variability in mouse microbiota analysis and implications for sample size determination.

Lab animal·2025
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 Experiment Video

Updated: Jan 24, 2026

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.5K

MeLSI: Metric Learning for Statistical Inference in Microbiome Community Composition Analysis.

Nathan Bresette, Aaron C Ericsson, Carter Woods

    Biorxiv : the Preprint Server for Biology
    |January 23, 2026
    PubMed
    Summary

    Metric Learning for Statistical Inference (MeLSI) identifies key microbes driving microbiome differences, improving analysis beyond fixed metrics. This machine learning approach offers interpretable insights for biomarker discovery.

    More Related Videos

    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

    17.8K
    Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
    09:55

    Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

    Published on: May 2, 2018

    28.1K

    Related Experiment Videos

    Last Updated: Jan 24, 2026

    Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
    09:57

    Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

    Published on: July 12, 2018

    12.5K
    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

    17.8K
    Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere
    09:55

    Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere

    Published on: May 2, 2018

    28.1K

    Area of Science:

    • Microbiome research
    • Machine learning
    • Statistical inference

    Background:

    • Current microbiome beta diversity analysis uses fixed distance metrics, treating all taxa equally.
    • This uniform approach may obscure subtle, biologically significant patterns driven by specific microbes.
    • Identifying key microbial drivers is crucial for discovering therapeutic targets and diagnostic biomarkers.

    Purpose of the Study:

    • To introduce MeLSI (Metric Learning for Statistical Inference), a novel machine learning framework for microbiome analysis.
    • To develop data-adaptive distance metrics that optimize detection of community composition differences.
    • To provide statistically rigorous and interpretable insights into microbiome data.

    Main Methods:

    • MeLSI employs an ensemble of weak learners with bootstrap and feature subsampling.
    • Gradient-based optimization is used to learn optimal feature weights for distance metrics.
    • Rigorous permutation testing ensures statistical inference, with learned metrics usable in PERMANOVA and PCoA.

    Main Results:

    • MeLSI maintains Type I error control and achieves competitive or superior F-statistics compared to traditional methods.
    • The framework provides interpretable feature-weight profiles, clarifying taxa driving group separation.
    • On the Atlas1006 dataset, MeLSI demonstrated stronger effect sizes and offered biological insights beyond fixed metrics.

    Conclusions:

    • MeLSI offers a statistically sound alternative to fixed metrics in microbiome beta diversity analysis.
    • The data-driven, interpretable nature of MeLSI enhances biological understanding and hypothesis generation.
    • MeLSI accelerates the translation of microbiome data into clinical applications and testable hypotheses.