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

Significance Testing: Overview01:04

Significance Testing: Overview

3.7K
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...
3.7K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.1K
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...
2.1K
Statgraphics01:10

Statgraphics

189
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
189
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.3K
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...
7.3K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.4K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.4K

You might also read

Related Articles

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

Sort by
Same author

A large dataset of brain imaging linked to health systems data: curation and access to a whole system national cohort from NHS Scotland.

GigaScience·2026
Same author

The GALENOS approach to triangulating evidence: a structured approach for integrating information from human and animal studies.

BMC medical research methodology·2026
Same author

Association of glomerular hyperfiltration with mortality in stroke: an analysis using pooled individual patient data.

European stroke journal·2026
Same author

A roadmap for establishing institutional readiness for cellular therapies targeting solid tumours and autoimmune diseases.

NPJ precision oncology·2026
Same author

Drivers of food cost in outer regional, remote, and very remote Australia: a systematic scoping review.

BMC public health·2026
Same author

Perihematomal Edema and Functional Outcome After Intracerebral Hemorrhage: A Meta-Analysis of Individual Participant Data.

Stroke·2026

Related Experiment Video

Updated: Sep 5, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.5K

Real-world Independent Testing of e-ASPECTS Software (RITeS): statistical analysis plan.

Grant Mair1, Francesca Chappell1, Chloe Martin1

  • 1Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH16 4SB, UK.

AMRC Open Research
|July 8, 2022
PubMed
Summary

This study validates artificial intelligence (AI) software for detecting ischemic stroke lesions and arterial occlusion. The AI tools, e-ASPECTS and e-CTA, will be rigorously tested against expert human interpretations in a large patient cohort.

Keywords:
CTautomated assessmentmachine learningstroke

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 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

2.4K

Related Experiment Videos

Last Updated: Sep 5, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 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

2.4K

Area of Science:

  • Neurology
  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) software shows potential for automated detection of ischemic stroke lesions and arterial occlusion.
  • AI tools can provide Alberta Stroke Program Early CT scores (ASPECTS) and collateral scores on CT and CTA, respectively.
  • Large-scale independent testing is crucial for informing the clinical adoption of these AI tools.

Purpose of the Study:

  • To conduct a comprehensive, large-scale, independent validation of e-ASPECTS and e-CTA software.
  • To assess the agreement between AI-derived and expert human interpretations of ASPECTS and collateral status.
  • To evaluate the diagnostic accuracy of e-ASPECTS for identifying stroke and the repeatability of AI tool results.

Main Methods:

  • Utilizing prospectively collected CT and CTA scans from over 6600 patients across 10 clinical stroke trials/registries.
  • Comparing AI software (e-ASPECTS, e-CTA) performance against independent expert human interpretation as the reference standard.
  • Conducting intention-to-analyse testing to assess agreement and diagnostic accuracy, including analysis of AI tool performance variations.

Main Results:

  • The study aims to quantify the agreement between software-generated ASPECTS and collateral scores versus expert human interpretations.
  • Diagnostic accuracy of e-ASPECTS in identifying all stroke causes will be determined using clinical follow-up and final opinions.
  • Secondary analyses will explore factors influencing e-ASPECTS accuracy and the repeatability of both e-ASPECTS and e-CTA.

Conclusions:

  • The RITeS study will provide robust, representative testing of e-ASPECTS and e-CTA.
  • This validation will be benchmarked against the current gold standard of expert human interpretation.
  • Findings will inform the clinical utility and reliability of AI in acute stroke assessment.