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: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

47.1K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
47.1K
Uncertainty: Overview00:59

Uncertainty: Overview

1.0K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.0K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

85.1K
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. 
85.1K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.9K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K
Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

75.3K
All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
75.3K

You might also read

Related Articles

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

Sort by
Same author

A Combinatorial Approach to Synthetic Data Generation for Machine Learning.

SN computer science·2026
Same author

Combinatorial Methods in Security Testing.

Computer·2024
Same author

It Doesn't Have to Be Like This: Cybersecurity Vulnerability Trends.

IT professional·2020
Same author

Finding Bugs in Cryptographic Hash Function Implementations.

IEEE transactions on reliability·2019
Same author

A Study on the Reuse of Plastic Concrete Using Extended Set-Retarding Admixtures.

Journal of research of the National Institute of Standards and Technology·2017
Same author

Repeatability and Reproducibility Standard Deviations in the Measurement of Trace Moisture Generated Using Permeation Tubes.

Journal of research of the National Institute of Standards and Technology·2016

Related Experiment Video

Updated: Sep 24, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.2K

True value and uncertainty in the GUM.

Raghu Kacker1

  • 1National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8910, USA.

Journal of Physics. Conference Series
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

The Guide to the Expression of Uncertainty in Measurement (GUM) operationalizes measurement uncertainty. Later JCGM documents, by focusing on coverage intervals, diverge from GUM by assuming a unique true value, which is often not applicable in metrology.

More Related Videos

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
07:57

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

Published on: April 29, 2014

13.5K
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

3.1K

Related Experiment Videos

Last Updated: Sep 24, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.2K
Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
07:57

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

Published on: April 29, 2014

13.5K
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
09:10

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Published on: July 12, 2022

3.1K

Area of Science:

  • Metrology
  • Measurement Science

Background:

  • The Guide to the Expression of Uncertainty in Measurement (GUM) defines uncertainty operationally.
  • Joint Committee for Guides in Metrology (JCGM) documents introduced the concept of a coverage interval.
  • Coverage intervals assume a unique true value for the measurand.

Purpose of the Study:

  • To clarify the operational definition of measurement uncertainty as presented in the GUM.
  • To highlight the limitations of the JCGM's coverage interval concept for ordinary measurands.
  • To correct misunderstandings regarding the GUM's treatment of true values and uncertainty.

Main Methods:

  • Conceptual analysis of metrology documents.
  • Comparison of GUM and JCGM approaches to measurement uncertainty.
  • Examination of the applicability of 'coverage interval' in metrology.

Main Results:

  • The GUM's operational view of uncertainty is distinct from the pre-GUM concept of a coverage interval.
  • The JCGM's coverage interval is of limited utility for ordinary measurands, which often lack a unique true value.
  • Divergence in JCGM documents stems from misinterpretations of GUM concepts.

Conclusions:

  • The operational definition of uncertainty in the GUM remains crucial for practical metrology.
  • The JCGM's coverage interval concept is not universally applicable to all measurands.
  • Revisiting the foundational principles of the GUM is necessary to resolve current ambiguities in uncertainty representation.