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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

97.6K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
97.6K
Random and Systematic Errors01:20

Random and Systematic Errors

13.8K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
13.8K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

5.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
5.4K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

10.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
10.6K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.1K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

851
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
851

You might also read

Related Articles

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

Sort by
Same author

Correction: Vector-borne disease surveillance and control resource needs in Colorado public health organizations.

PloS one·2026
Same author

"It is not something you like to hear, but it is something you have to know". Community preferences for risk communication during pregnancy in the context of an emerging pathogen: a multicountry qualitative study with women in three ZIKV endemic countries who were pregnant during and following ZIKV.

BMJ global health·2026
Same author

Evaluating Estimators in Partially Identified Models.

Epidemiology (Cambridge, Mass.)·2026
Same author

Comparative effectiveness of alternative times to opioid agonist treatment taper initiation on taper completion and all-cause mortality among people with opioid use disorder: A retrospective population-based target trial emulation study in British Columbia, Canada, 2010-2020.

Addiction (Abingdon, England)·2026
Same author

Noninflammatory hypotrichosis in white-tailed deer and raccoons in the eastern United States.

Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc·2026
Same author

Prediction of exposure to chrysotile asbestos fibers among Quebec miners and millers based on impinger measurements.

Annals of work exposures and health·2026

Related Experiment Video

Updated: Oct 26, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K

Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to

Harlan Campbell1, Valentijn M T de Jong2, Lauren Maxwell3

  • 1Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.

Research Synthesis Methods
|July 27, 2021
PubMed
Summary

This study addresses meta-analyses with measurement error in exposure variables. A Bayesian framework provides accurate estimates, even with imperfect data, improving reliability of findings.

Keywords:
Bayesian evidence synthesismeasurement errormeta-analysismisclassificationpartial identification

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

3.5K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

818

Related Experiment Videos

Last Updated: Oct 26, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.2K
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

3.5K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

818

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Meta-analyses ideally use unbiased study data.
  • Non-differential measurement error in exposure variables can compromise meta-analysis results.
  • Accurate estimation of associations is crucial in research synthesis.

Purpose of the Study:

  • To develop a statistical framework for meta-analysis when exposure variables have non-differential measurement error.
  • To provide methods for obtaining reliable point and interval estimates in such scenarios.
  • To explore adjustments for covariates using individual-participant data (IPD).

Main Methods:

  • A flexible Bayesian meta-analysis framework is proposed.
  • The model accommodates varying degrees of prior knowledge about measurement error magnitude.
  • Methods are demonstrated for continuous outcomes and continuous exposure variables using regression coefficients.

Main Results:

  • The Bayesian framework yields appropriate point and interval estimates despite measurement error.
  • The model's performance is evaluated across different levels of prior information on measurement error.
  • Demonstration of adjusting for covariates using IPD in the meta-analysis model.

Conclusions:

  • The developed Bayesian approach effectively handles non-differential measurement error in meta-analyses.
  • This method enhances the accuracy of estimated associations in research synthesis.
  • The framework offers flexibility and can incorporate individual-participant data for robust analysis.