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

Test for Homogeneity01:23

Test for Homogeneity

2.3K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.3K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

294
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...
294
Variability: Analysis01:11

Variability: Analysis

374
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
374
Behrens–Fisher Test00:57

Behrens–Fisher Test

212
The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test...
212
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.9K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.9K
Stratified Sampling Method01:16

Stratified Sampling Method

14.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.

Human brain mapping·2026
Same author

An open, fully-processed data resource for studying mood and sleep variability in the developing brain.

Aperture neuro·2026
Same author

Patterns of brain-wide associations reflect socioeconomics.

Science (New York, N.Y.)·2026
Same author

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible and Clinically Relevant Brain-Wide Motifs.

bioRxiv : the preprint server for biology·2026
Same author

Frame-wise multi-echo distortion correction for superior functional MRI.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Trajectories of hippocampal subregion development in the first years of life and their association with school-aged episodic memory outcomes.

bioRxiv : the preprint server for biology·2026
Same journal

Impact of Transcranial Direct Current Stimulation-Induced Electric Fields on Slowing Cognitive Decline in Older Adults With Mild Cognitive Impairment or Remitted Major Depressive Disorder: An Analysis of the PACt-MD Randomized Clinical Trial.

Biological psychiatry·2026
Same journal

Remembering Jon-Kar Zubieta, M.D., Ph.D.

Biological psychiatry·2026
Same journal

Kappa opioid receptor availability in borderline personality disorder: An in-vivo investigation with [<sup>11</sup>C]EKAP PET imaging.

Biological psychiatry·2026
Same journal

From Satiety to Substance Use: Neural Mechanisms of GLP-1 Signaling in Appetite and Reward.

Biological psychiatry·2026
Same journal

Distinct and Shared Molecular Mechanisms Underlie Morphological-Functional Overcoupling and Undercoupling in Major Depressive Disorder.

Biological psychiatry·2026
Same journal

Dynamic Brain States With Cannabis Intoxication: Beyond "More Is Better" in Interpreting Brain Connectivity.

Biological psychiatry·2026
See all related articles

Related Experiment Video

Updated: Dec 22, 2025

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.0K

Methods and Challenges for Assessing Heterogeneity.

Eric Feczko1, Damien A Fair2

  • 1Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.

Biological Psychiatry
|May 11, 2020
PubMed
Summary
This summary is machine-generated.

Mental health research faces challenges from the homogeneity assumption. New statistical frameworks can differentiate between comorbidity and heterogeneity, advancing diagnosis and treatment for mental health disorders.

Keywords:
Bifactor modelingComorbidityFunctional random forestHeterogeneityNormative modelingPsychopathology

More Related Videos

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
11:34

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

13.0K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

19.3K

Related Experiment Videos

Last Updated: Dec 22, 2025

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.0K
Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers
11:34

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

13.0K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

19.3K

Area of Science:

  • Psychiatry and Psychology
  • Statistical Modeling

Background:

  • The assumption of homogeneity in mental health disorders hinders progress in diagnosis, mechanistic understanding, and treatment development.
  • This assumption leads to distinct problems: comorbidity (multiple disorders) and heterogeneity (variability within disorders).

Purpose of the Study:

  • To explore the contributions of unifying (comorbidity-focused) and multifactorial (heterogeneity-focused) approaches in mental health research.
  • To introduce statistical frameworks that can integrate and differentiate between these two approaches.

Main Methods:

  • The study discusses unifying approaches assuming a general psychopathology factor.
  • It also covers multifactorial approaches positing discrete factors for subtypes.
  • Statistical frameworks like bifactor models, normative modeling, and functional random forests are presented.

Main Results:

  • Unifying approaches can yield tools for rapid case assessment and triage.
  • Multifactorial approaches can identify treatment-responsive subtypes and distinct underlying mechanisms.
  • Integrated frameworks can determine the relative influence of heterogeneity versus comorbidity on sets of disorders.

Conclusions:

  • Both unifying and multifactorial approaches offer valuable, albeit different, contributions to mental health.
  • Statistical frameworks integrating both perspectives are crucial for advancing the understanding of psychopathology.
  • Future research should utilize these frameworks to gain deeper insights into the nature of mental health disorders.