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: Overview00:59

Uncertainty: Overview

842
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.
842
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

971
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...
971
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.4K
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...
4.4K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

777
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...
777
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

76.4K
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. 
76.4K
The Uncertainty Principle04:08

The Uncertainty Principle

23.7K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
23.7K

You might also read

Related Articles

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

Sort by
Same author

A "PalliPulm" framework to improve palliative care education and practice in pulmonary-critical care medicine: an official American Thoracic Society Workshop Report.

Annals of the American Thoracic Society·2026
Same author

Strategies for Inclusive Practices in Clinical Procedure Training.

The clinical teacher·2026
Same author

Moving from "Me" to "We": Adopting a Collectivist Approach to Address the Wicked Problem of Trainee and Faculty Back-up/Jeopardy Systems.

Journal of general internal medicine·2026
Same author

How do Internal Medicine Residents from Different Backgrounds Make Subspecialty Career Choices? A Qualitative Analysis.

Journal of general internal medicine·2025
Same author

In response: "The core competencies in hospital medicine: Procedures 2025 update".

Journal of hospital medicine·2025
Same author

Faculty Development for the People: Workplace-Based Solutions for Busy Clinicians.

The clinical teacher·2025

Related Experiment Video

Updated: Aug 29, 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

9.1K

Teaching the science of uncertainty.

Glenn Moulder1, Emily Harris2, Lekshmi Santhosh2

  • 1Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.

Diagnosis (Berlin, Germany)
|September 10, 2022
PubMed
Summary

Clinical educators must design curricula to address uncertainty, as it is frequently encountered but rarely discussed explicitly. Explicitly teaching diagnostic and prognostic uncertainty improves diagnostic reasoning, accuracy, and patient care across all training levels.

Keywords:
clinical reasoningdiagnosisgraduate medical educationuncertaintyundergraduate medical education

More Related Videos

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.3K
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

Related Experiment Videos

Last Updated: Aug 29, 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

9.1K
Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.3K
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

Area of Science:

  • Medical Education
  • Clinical Reasoning
  • Healthcare Communication

Background:

  • Uncertainty is a pervasive element in clinical practice, impacting diagnostic processes and patient care.
  • Current medical education often implicitly addresses uncertainty, hindering explicit discussion and comfort with this concept.
  • There's a growing need to formally define and measure diagnostic uncertainty in medicine.

Approach:

  • This article reviews the current state of teaching clinical uncertainty in undergraduate medical education (UME) and graduate medical education (GME) curricula.
  • It explores strategies for explicitly discussing diagnostic and prognostic uncertainty with trainees.
  • The focus is on developing curricula that enhance trainees' ability to manage and communicate uncertainty.

Key Points:

  • Explicitly discussing and fostering comfort with uncertainty can significantly improve diagnostic reasoning and accuracy.
  • Addressing uncertainty is crucial for effective shared decision-making between clinicians and patients.
  • Curricula should integrate the science of uncertainty for all levels of medical trainees, from UME to GME.

Conclusions:

  • Designing and implementing curricula that explicitly address clinical uncertainty is essential for medical educators.
  • Improved understanding and communication of uncertainty can lead to better patient outcomes and enhanced clinical practice.
  • Strategies for teaching uncertainty should be tailored to all stages of medical training.