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

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.7K
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...
26.7K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.0K
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...
2.0K
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

8.5K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
8.5K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

266
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%...
266
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

190
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,...
190
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.7K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.

Alcohol research : current reviews·2026
Same author

A framework of digital biomarkers for neurodegenerative diseases.

Nature reviews bioengineering·2026
Same author

SocialGen: Modeling Multi-Human Social Interaction with Language Models.

Proceedings. International Conference on 3D Vision·2026
Same author

Mapping Individualized Developmental Imbalance in Youth and Its Association with Psychopathology.

bioRxiv : the preprint server for biology·2026
Same author

Using deep learning to identify brain networks mediating cognitive and motor impairments in alcohol use disorder.

Translational psychiatry·2026
Same author

A generalized synthetic control algorithm for sparse functional data.

bioRxiv : the preprint server for biology·2026
Same journal

Cycle Diffusion Model for Counterfactual Image Generation.

Predictive Intelligence in Medicine. PRIME (Workshop)·2026
Same journal

Neurocognitive Latent Space Regularization for Multi-Label Diagnosis from MRI.

Predictive Intelligence in Medicine. PRIME (Workshop)·2025
Same journal

Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging.

Predictive Intelligence in Medicine. PRIME (Workshop)·2024
Same journal

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification.

Predictive Intelligence in Medicine. PRIME (Workshop)·2024
Same journal

Imputing Brain Measurements Across Data Sets via Graph Neural Networks.

Predictive Intelligence in Medicine. PRIME (Workshop)·2023
Same journal

Multiple Instance Neuroimage Transformer.

Predictive Intelligence in Medicine. PRIME (Workshop)·2022
See all related articles

Related Experiment Video

Updated: Aug 22, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

42.2K

Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing.

Magdalini Paschali1, Qingyu Zhao1, Ehsan Adeli1

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.

Predictive Intelligence in Medicine. PRIME (Workshop)
|November 7, 2022
PubMed
Summary
This summary is machine-generated.

This study integrates deep learning with hypothesis testing to identify risk factors for depression symptoms in adolescents. The novel approach links data-driven predictions to statistical significance, enhancing neurodevelopmental research.

Keywords:
Behavioral DataClassificationDisease PredictionOutcome PredictionPermutation TestingRisk Factor Identification

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

72.5K

Related Experiment Videos

Last Updated: Aug 22, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models
12:59

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

42.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

72.5K

Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • Traditional neuroscience research often tests hypotheses using neuropsychological and behavioral measures.
  • Deep learning offers a data-driven approach to predict outcomes and identify influential factors.
  • Current deep learning methods lack statistical significance, limiting their impact on hypothesis testing.

Purpose of the Study:

  • To integrate hypothesis testing into deep learning analyses for neuroscience research.
  • To develop a flexible and scalable approach combining deep learning with permutation testing.
  • To identify statistically significant risk factors associated with depression symptoms in adolescents.

Main Methods:

  • Developed a novel approach integrating permutation testing with deep learning.
  • Applied the method to yearly self-reported data from 621 adolescents in the National Consortium of Alcohol and Neurodevelopment in Adolescence (NCANDA) study.
  • Used the approach to predict negative valence, a symptom of major depressive disorder (MDD) per NIMH Research Domain Criteria (RDoC).

Main Results:

  • The integrated approach successfully predicted negative valence in adolescents.
  • Identified categories of risk factors that significantly explain the depression symptom.
  • Demonstrated the utility of linking deep learning predictions with statistical hypothesis testing.

Conclusions:

  • The proposed method offers a statistically rigorous way to apply deep learning in neuroscience.
  • This approach enhances the interpretability and impact of data-driven findings in understanding mental health conditions.
  • Facilitates the identification of specific risk factors contributing to adolescent depression.