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

Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

860
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
860
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

441
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
441
Qualitative Analysis03:46

Qualitative Analysis

25.0K
For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...
25.0K
Dimensional Analysis03:40

Dimensional Analysis

65.3K
Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
65.3K
Dimensional Analysis01:27

Dimensional Analysis

687
Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
687
Pedigree Analysis01:35

Pedigree Analysis

89.8K
Overview
89.8K

You might also read

Related Articles

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

Sort by
Same author

Rethinking bioinformatics expertise in the era of artificial intelligence.

NPJ digital medicine·2026
Same author

Large Language Models and Their Applications in Mental Health: Scoping Review.

JMIR mental health·2026
Same author

Examination of shared gut microbiome signatures in aging and Parkinson's disease.

Frontiers in aging neuroscience·2026
Same author

Blood plasma proteomic biomarkers for forecasting transition to psychosis in an Asian cohort.

Translational psychiatry·2026
Same author

Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search.

Briefings in bioinformatics·2026
Same author

Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study.

JMIR formative research·2025
Same journal

Make uphill thermodynamics downhill in pathway design.

Trends in biotechnology·2026
Same journal

Engineering a capture-bioremediate-release microbial biofilm for simultaneous bioremediation of microplastics and adsorbed heavy metals.

Trends in biotechnology·2026
Same journal

Engineered bacterial biofilms for biotechnological applications.

Trends in biotechnology·2026
Same journal

Multiscale and programmable engineering of edible mushroom mycelium-based materials.

Trends in biotechnology·2026
Same journal

Transporter engineering in microbial cell factories.

Trends in biotechnology·2026
Same journal

Random integration and high-throughput screening forging robust microbial cell factories.

Trends in biotechnology·2026
See all related articles

Related Experiment Video

Updated: Feb 14, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Dealing with Confounders in Omics Analysis.

Wilson Wen Bin Goh1, Limsoon Wong2

  • 1School of Biological Sciences, Nanyang Technological University, 637551, Singapore.

Trends in Biotechnology
|February 25, 2018
PubMed
Summary
This summary is machine-generated.

The Anna Karenina effect describes statistical issues in biological data analysis where confounders lead to incorrect results. Addressing hypothesis construction, null distribution, and test statistics can improve feature selection for disease phenotypes.

Keywords:
Statisticsbiomarkerfeature selectiongeneralizabilityreproducibility

More Related Videos

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii
07:51

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

8.2K
Dissection of Drosophila melanogaster Flight Muscles for Omics Approaches
08:33

Dissection of Drosophila melanogaster Flight Muscles for Omics Approaches

Published on: October 17, 2019

12.9K

Related Experiment Videos

Last Updated: Feb 14, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K
A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii
07:51

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

8.2K
Dissection of Drosophila melanogaster Flight Muscles for Omics Approaches
08:33

Dissection of Drosophila melanogaster Flight Muscles for Omics Approaches

Published on: October 17, 2019

12.9K

Area of Science:

  • * Statistical analysis in bioinformatics and computational biology.
  • * Data science and machine learning applications in biological research.

Background:

  • * The Anna Karenina effect highlights the gap between theoretical statistics and real-world data application.
  • * In biological data analysis, this effect causes rejection of the null hypothesis due to confounders, not biological relevance.
  • * Current statistical methods often fail to adequately address confounders, relying heavily on P-value manipulation.

Purpose of the Study:

  • * To explain the Anna Karenina effect in the context of biological data analysis.
  • * To propose mechanistic solutions for addressing confounders in statistical testing.
  • * To improve the selection of phenotypically relevant biological features.

Main Methods:

  • * Discussion of three key mechanistic elements in statistical testing: hypothesis statement construction, null distribution appropriateness, and test-statistic construction.
  • * Analysis of how these elements contribute to or can mitigate the Anna Karenina effect.
  • * Suggestion of design principles to counteract the effect.

Main Results:

  • * Identification of confounders as a primary driver of the Anna Karenina effect in biological data.
  • * Demonstration that P-value manipulation alone is insufficient to resolve this issue.
  • * Proposed modifications to statistical test mechanics to improve feature selection accuracy.

Conclusions:

  • * Statistical tests must be designed to actively resolve confounders, not just manage P-values.
  • * Improving hypothesis construction, null distribution selection, and test statistics can effectively counter the Anna Karenina effect.
  • * This approach enhances the identification of biologically and phenotypically relevant features.