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 Package for the Social Sciences (SPSS)01:22

Statistical Package for the Social Sciences (SPSS)

1.2K
The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...
1.2K
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 Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

927
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
927
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

1.6K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
1.6K
Psychology as a Science01:13

Psychology as a Science

3.8K
Psychology, as a scientific discipline, aims to understand the mind and behavior through rigorous and systematic methods. The foundation of psychological research is evidence-based, relying heavily on the scientific method to derive and validate knowledge. This structured approach ensures that findings are reliable, valid, and applicable to broader contexts.
The scientific method in psychology involves six critical steps: making observations, formulating hypotheses, conducting tests, analyzing...
3.8K
Probability in Statistics01:14

Probability in Statistics

22.4K
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...
22.4K

You might also read

Related Articles

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

Sort by
Same author

Guidelines for preventing and reporting contamination in low-biomass microbiome studies.

Nature microbiology·2025
Same author

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress.

eLife·2025
Same author

Planning and describing a microbiome data analysis.

Nature microbiology·2025
Same author

Host DNA depletion on frozen human respiratory samples enables successful metagenomic sequencing for microbiome studies.

Communications biology·2024
Same author

Host DNA depletion on frozen human respiratory samples enables successful metagenomic sequencing for microbiome studies.

Research square·2024
Same author

Analyzing microbial evolution through gene and genome phylogenies.

Biostatistics (Oxford, England)·2023
Same journal

Global distribution of isoprenoid quinones across Bacteria.

mSystems·2026
Same journal

Environmental former <i>Massilia</i> group bacteria secrete metabolites that promote <i>Leptospira</i> growth.

mSystems·2026
Same journal

Signatures in the gut microbiome of German elite athletes: insights from a matched-subgroup analysis.

mSystems·2026
Same journal

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

mSystems·2026
Same journal

Disentangling production and persistence of extracellular virions in grassland soils with SIP-viromics.

mSystems·2026
Same journal

Microbial consortia mediating lignocellulose turnover and denitrification in eutrophic lake sediment enrichments.

mSystems·2026
See all related articles

Related Experiment Video

Updated: Jan 24, 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

17.8K

Rigorous Statistical Methods for Rigorous Microbiome Science.

Amy D Willis1

  • 1Department of Biostatistics, University of Washington, Seattle, Washington, USA adwillis@uw.edu.

Msystems
|May 30, 2019
PubMed
Summary
This summary is machine-generated.

Rigorous statistical methods are crucial for microbiome and biodiversity data analysis. Careful validation and modeling reduce false discoveries, enhancing scientific understanding in high-throughput sequencing studies.

Keywords:
hypothesis testingmachine learningmodelingreproducibilitystatistics

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

1.1K
A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
08:14

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model

Published on: February 28, 2018

9.3K

Related Experiment Videos

Last Updated: Jan 24, 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

17.8K
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

1.1K
A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
08:14

A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model

Published on: February 28, 2018

9.3K

Area of Science:

  • Microbiome science and biodiversity research.

Background:

  • High-throughput sequencing advances microbiome discovery.
  • Distinguishing genuine findings from spurious signals is a key challenge.

Purpose of the Study:

  • To present rigorous statistical methods and software for microbiome and biodiversity data analysis.
  • To emphasize the importance of valid P values in scientific discovery.

Main Methods:

  • Development of statistical methods for analyzing microbiome and biodiversity data.
  • Focus on thoughtful modeling and careful validation of statistical approaches.

Main Results:

  • Statistical methods that produce valid P values are essential.
  • Rigorous analysis minimizes the risk of false discoveries.

Conclusions:

  • Careful statistical analysis is vital for advancing scientific understanding in microbiome research.
  • The Statistical Diversity Lab provides tools to improve the reliability of microbiome data interpretation.