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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Self-Report Tests of Personality01:22

Self-Report Tests of Personality

Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...

You might also read

Related Articles

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

Sort by
Same author

High-resolution geodetic velocities reveal role of weak faults in deformation of Tibetan Plateau.

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

IDF diabetes Atlas: A worldwide review of studies utilizing retinal photography to screen for diabetic retinopathy from 2017 to 2024 inclusive.

Diabetes research and clinical practice·2025
Same author

Concurrent nonavalent human papillomavirus (HPV) vaccination and immune stimulation with imiquimod to treat recalcitrant HPV-associated high grade vaginal intra-epithelial neoplasia.

Gynecologic oncology reports·2024
Same author

A Quantitative Method to Measure the Kinetics of Elemental Mercury Emissions From Black Shale (Nova Scotia, Canada).

Bulletin of environmental contamination and toxicology·2023
Same author

Computing committors via Mahalanobis diffusion maps with enhanced sampling data.

The Journal of chemical physics·2022
Same author

Meta-analysis of the outcomes of Trans Rectus Sheath Extra-Peritoneal Procedure (TREPP) for inguinal hernia. Author's reply.

Hernia : the journal of hernias and abdominal wall surgery·2022

Related Experiment Videos

The development of a tool to predict team performance.

M A Sinclair1, C E Siemieniuch, R A Haslam

  • 1Centre for Innovative & Collaborative Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom. murray.sinclair@me.com

Applied Ergonomics
|June 14, 2011
PubMed
Summary

A new tool quantifies team success in process execution for systems engineering. Prioritizing Human Factors Integration significantly boosts team success likelihood, while neglecting it drastically reduces it.

Related Experiment Videos

Area of Science:

  • Systems Engineering
  • Human Factors Integration
  • Team Performance Modeling

Background:

  • Systems engineers require tools to predict team success during early design phases.
  • Assessing the impact of team structure and qualifications on process execution is crucial.
  • Existing methods may not quantitatively predict team success in complex systems.

Purpose of the Study:

  • To develop and validate a quantitative tool for predicting team success in process execution.
  • To enable "what-if" analyses regarding team size and qualification requirements.
  • To highlight the critical role of Human Factors Integration in achieving team success.

Main Methods:

  • Development of a predictive tool for team success.
  • Verification and validation of the tool's predictive accuracy.
  • Simulation modeling to assess the impact of Human Factors Integration.

Main Results:

  • The developed tool demonstrates fair predictive accuracy and shows promise.
  • Human Factors Integration is a critical determinant of team success, with likelihoods exceeding 0.95 when fully implemented.
  • Neglecting Human Factors Integration can reduce team success likelihood to as low as 0.05.
  • Even with optimal individuals, team success probability is limited to 0.35 without strong Human Factors Integration.

Conclusions:

  • The tool provides a valuable method for quantitatively assessing team success in systems design.
  • Human Factors Integration is paramount for maximizing team performance and achieving project objectives.
  • Findings underscore the need for significant attention to Human Factors Integration throughout the systems design lifecycle.