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

Decision Making: P-value Method01:09

Decision Making: P-value Method

6.4K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.4K
P-value01:10

P-value

7.9K
P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
7.9K
Significance Testing: Overview01:04

Significance Testing: Overview

8.8K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
8.8K
Bonferroni Test01:10

Bonferroni Test

3.1K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.1K
Variability: Analysis01:11

Variability: Analysis

280
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...
280
Test for Homogeneity01:23

Test for Homogeneity

2.2K
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...
2.2K

You might also read

Related Articles

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

Sort by
Same author

A Digital Toolkit for Weight Loss Maintenance in European Adults (NoHoW): 2×2 Factorial Randomized Controlled Trial.

Journal of medical Internet research·2026
Same author

Association of shift work with body weight, stress, sleep, and dietary intake in prison officers and firefighters.

European journal of nutrition·2026
Same author

Big breakfast diet composition impacts on appetite control and gut health: a randomised weight loss trial in adults with overweight or obesity.

The British journal of nutrition·2026
Same author

Physical Exercise or Cognitive Behavioral Therapy for Takotsubo Cardiomyopathy: A Randomized Controlled Trial.

Circulation. Heart failure·2025
Same author

Exercise and eating motivation in weight loss maintenance: revisiting the motivational spillover with the NoHoW study.

Annals of behavioral medicine : a publication of the Society of Behavioral Medicine·2025
Same author

Aging Gut-Brain Interactions: Pro-Inflammatory Gut Bacteria Are Elevated in Fecal Samples from Individuals Living with Alzheimer's Dementia.

Geriatrics (Basel, Switzerland)·2025

Related Experiment Video

Updated: Nov 17, 2025

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

33.9K

p value variability and subgroup testing.

Graham Horgan1

  • 1Biomathematics and Statistics Scotland, Aberdeen, Scotland. g.horgan@abdn.ac.uk.

European Journal of Nutrition
|February 15, 2021
PubMed
Summary

P values in health and nutrition studies show significant variability and randomness. Always test interaction terms when examining subgroups separately to ensure accurate evidence interpretation.

Area of Science:

  • Nutritional science
  • Health research methodology
  • Statistical analysis in medicine

Background:

  • P values are the primary metric for statistical significance in health and nutrition research.
  • The inherent variability and randomness of p values can impact the reliability of study findings.
  • Subgroup analyses are common but require careful statistical consideration.

Purpose of the Study:

  • To highlight the variability and randomness associated with p values.
  • To emphasize the critical need for testing interaction terms in subgroup analyses.
  • To improve the interpretation of evidence in nutritional and health studies.

Main Methods:

  • The article provides a conceptual discussion of p value properties.
  • It reviews statistical principles related to subgroup analysis and interaction testing.
Keywords:
InteractionSubgroupp value

More Related Videos

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.3K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.4K

Related Experiment Videos

Last Updated: Nov 17, 2025

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

33.9K
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.3K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.4K
  • No new empirical data were generated; it is a methodological review.

    • Main Results:

    • P values exhibit inherent randomness, affecting their consistency as a measure of evidence.
    • Failure to test interaction terms can lead to spurious findings when subgroups are analyzed independently.
    • Proper statistical practice is essential for valid subgroup interpretation.

    Conclusions:

    • Researchers must acknowledge the probabilistic nature of p values.
    • Interaction terms are crucial for valid subgroup comparisons in nutritional and health studies.
    • Adherence to rigorous statistical methods enhances the credibility of research findings.