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
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.9K
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
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.5K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.5K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.9K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.9K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

8.0K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Pheochromocytoma-Induced Hypertension After Traumatic Brain Injury.

Cureus·2023
Same author

Actigraphic and nursing sleep log measures in moderate-to-severe traumatic brain injury: Identifying discrepancies in total sleep time.

PM & R : the journal of injury, function, and rehabilitation·2022
Same author

Post-hoc power analysis: a conceptually valid approach for power based on observed study data.

General psychiatry·2022
Same author

Relationship between Omnibus and Post-hoc Tests: An Investigation of performance of the F test in ANOVA.

Shanghai archives of psychiatry·2018
Same author

Sample Size Calculations for Comparing Groups with Binary Outcomes.

Shanghai archives of psychiatry·2017
Same author

The Value of Palliative Gastrectomy for Gastric Cancer Patients With Intraoperatively Proven Peritoneal Seeding.

Medicine·2015

Related Experiment Video

Updated: Jan 4, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.2K

Homoscedasticity: an overlooked critical assumption for linear regression.

Kun Yang1, Justin Tu2, Tian Chen3

  • 1Department of Family Medicine and Public Health, University of California System, San Diego, California, USA.

General Psychiatry
|November 2, 2019
PubMed
Summary

Homoscedasticity, often overlooked in linear regression, significantly impacts result validity more than normality. This study highlights its critical importance for reliable biomedical and psychosocial research findings.

Keywords:
Analysis of VarianceF-testNormal Distributiont-test

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K

Related Experiment Videos

Last Updated: Jan 4, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K

Area of Science:

  • Biostatistics
  • Statistical modeling
  • Psychosocial research

Background:

  • Linear regression is a common statistical tool in biomedical and psychosocial research.
  • Homoscedasticity, the assumption of equal variance, is frequently overlooked compared to normality.
  • The impact of violating homoscedasticity on linear regression validity is often underestimated.

Purpose of the Study:

  • To investigate the impact of homoscedasticity violation on linear regression inference.
  • To compare the effect of homoscedasticity versus normality violations.
  • To emphasize the importance of checking homoscedasticity in statistical analyses.

Main Methods:

  • Monte Carlo simulation studies were employed.
  • Linear regression models were fitted under various conditions.
  • The validity of statistical inference was assessed.

Main Results:

  • Violation of homoscedasticity has a greater impact on the validity of linear regression results than violation of normality.
  • Overlooking homoscedasticity can lead to erroneous conclusions in research.
  • Simulation results quantify the differential effects of these assumption violations.

Conclusions:

  • Homoscedasticity is a critical assumption in linear regression that requires careful attention.
  • Researchers should prioritize assessing homoscedasticity to ensure the reliability of their findings.
  • This study underscores the practical implications of statistical assumptions in applied research.