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

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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% chance...
Bonferroni Test01:10

Bonferroni Test

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...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Significance Testing: Overview01:04

Significance Testing: Overview

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...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...

You might also read

Related Articles

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

Sort by
Same author

Complexity does not create precision; the case of counting the number of vapers.

Global epidemiology·2026
Same author

Multiple cause of death during the COVID-19 pandemic: a population study In Colombia and Brazil.

International journal of public health·2026
Same author

Wildfire smoke PM<sub>2.5</sub> as a growing source of emission and the challenges for current air quality regulations.

Environment international·2026
Same author

Improving the description of racial and ethnic variation in socioeconomic health differentials.

American journal of epidemiology·2026
Same author

Excess mortality following discharge from substance use disorder treatment in Chile.

Addiction (Abingdon, England)·2026
Same author

Screening for Breast Cancer.

JAMA·2026
Same journal

Explaining biological differences between men and women by gendered mechanisms.

Emerging themes in epidemiology·2023
Same journal

Population cause of death estimation using verbal autopsy methods in large-scale field trials of maternal and child health: lessons learned from a 20-year research collaboration in Central Ghana.

Emerging themes in epidemiology·2023
Same journal

Dynamics of COVID-19 progression and the long-term influences of measures on pandemic outcomes.

Emerging themes in epidemiology·2022
Same journal

Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research.

Emerging themes in epidemiology·2022
Same journal

Geographical clustering and geographically weighted regression analysis of home delivery and its determinants in developing regions of Ethiopia: a spatial analysis.

Emerging themes in epidemiology·2022
Same journal

Candida and the Gram-positive trio: testing the vibe in the ICU patient microbiome using structural equation modelling of literature derived data.

Emerging themes in epidemiology·2022
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

(Errors in statistical tests)3.

Carl V Phillips1, Richard F MacLehose, Jay S Kaufman

  • 1Department of Epidemiology, UNC School of Public Health, University of North Carolina, Pittsboro Street, Chapel Hill, NC 27599-7435, USA. carl.v.phillips@ualberta.ca

Emerging Themes in Epidemiology
|July 16, 2008
PubMed
Summary
This summary is machine-generated.

Statistical analysis of p-values revealed a non-uniform distribution of terminal digits, challenging previous findings. This meta-critique highlights the need for careful interpretation of statistical significance and improved data precision.

More Related Videos

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

Related Experiment Videos

Last Updated: Jul 3, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

Area of Science:

  • Statistics
  • Scientific Publishing
  • Research Methodology

Background:

  • A 2004 study by Garcia-Berthou and Alcaraz questioned statistical reporting in medical papers, noting non-uniform p-value digit distributions.
  • Jeng (2006) critiqued this, arguing the original analysis incorrectly used a continuous distribution for discrete digits, questioning the evidence of digit preference.
  • This exchange highlights ongoing debates about statistical significance testing and the interpretation of p-values in scientific literature.

Purpose of the Study:

  • To conduct a meta-critique of the statistical analysis and interpretation in the Garcia-Berthou/Alcaraz and Jeng exchange.
  • To investigate the distribution of terminal digits in p-values with additional data and improved statistical precision.
  • To re-evaluate the claims of digit preference and their implications for statistical significance.

Main Methods:

  • Re-analyzed the statistical methods used in prior critiques of p-value digit distributions.
  • Collected additional data to enhance statistical precision.
  • Analyzed the frequency distribution of terminal p-value digits in both original and combined datasets.

Main Results:

  • The combined dataset showed a clear divergence from a uniform distribution of terminal p-value digits.
  • Observed variations in digit frequencies within the additional data, revealing a distinctive pattern.
  • The findings suggest explanations other than calculation or transcription errors for the observed divergence.

Conclusions:

  • The meta-critique found issues with the previous analyses and their conclusions regarding p-value digit distributions.
  • Additional data confirm a non-uniform distribution, but the cause is unlikely to be simple errors.
  • Emphasizes the need to move beyond a simplistic view of statistical significance and improve data precision in research.