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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.6K
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...
5.6K
What Are Outliers?01:12

What Are Outliers?

3.6K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
3.6K
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

1.9K
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...
1.9K
Significance Testing: Overview01:04

Significance Testing: Overview

3.3K
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...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Adherence to the Mediterranean diet and risk of pancreatic cancer: an analysis of 2.3 million participants in the Pooling Project of Prospective Studies of Diet and Cancer (DCPP).

European journal of epidemiology·2026
Same author

GDF11 supplementation improved retinal structure and function in retinal ischemia injury.

Advances in ophthalmology practice and research·2026
Same authorSame journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026
Same author

The association between PM<sub>2.5</sub> components and cognitive decline: the impact of measurement error correction.

Environmental research·2026
Same author

Prospective study of reproductive span and menopausal hormone therapy and cognitive decline over 8 years in the Nurses' Health Study.

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

Long-term adherence and changes in the Mediterranean and MIND diets in relation to dementia risk and cognitive function.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026
Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026
Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
00:08

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.0K

Hypothesis testing for detecting outlier evaluators.

Li Xu1, David M Zucker2, Molin Wang1,3,4

  • 1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

The International Journal of Biostatistics
|November 1, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage method to identify outlier evaluators in epidemiological research. The procedure effectively detects inconsistent measurements, improving data reliability in studies like hearing loss risk factor analysis.

Keywords:
audiometric dataevaluator outliersoutlier detectionquality control

More Related Videos

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.2K
Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

11.8K

Related Experiment Videos

Last Updated: Jun 8, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
00:08

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

7.0K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.2K
Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

11.8K

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Epidemiological studies rely on measurements from multiple evaluators.
  • Variability in evaluator performance can impact study outcomes and data integrity.

Purpose of the Study:

  • To propose and validate a statistical procedure for detecting outlier evaluators in epidemiological data.
  • To enhance the reliability of disease outcome measurements in large-scale studies.

Main Methods:

  • A two-stage statistical procedure was developed.
  • Stage one involves fitting a regression model to estimate evaluator effects.
  • Stage two employs stepwise hypothesis testing to identify outliers.

Main Results:

  • The proposed procedure demonstrates effectiveness in identifying outlier evaluators.
  • Simulation studies assessed the true positive and true negative rates of the method.
  • The method was successfully applied to identify potential outlier audiologists in a hearing loss study.

Conclusions:

  • The developed two-stage procedure is a reliable tool for detecting outlier evaluators in epidemiological research.
  • Accurate identification of outlier evaluators can improve the quality and validity of epidemiological findings.
  • This method contributes to more robust data analysis in public health research.