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

81.9K
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. 
81.9K
Data Validation01:15

Data Validation

252
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
252
Random and Systematic Errors01:20

Random and Systematic Errors

12.6K
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...
12.6K
Accuracy and Precision01:52

Accuracy and Precision

11.3K
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.  Highly accurate...
11.3K
Variability: Analysis01:11

Variability: Analysis

192
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
192
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

311
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
311

You might also read

Related Articles

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

Sort by
Same author

Inflammatory Biomarkers Predicting Major Adverse Cardiovascular Events in People Living With HIV: A Systematic Review and Meta-Analysis.

Journal of the International AIDS Society·2026
Same author

The applicability to systematic reviews of common effect, random effects and fixed effects approaches to meta-analysis.

Statistical methods in medical research·2026
Same author

Predicting hypotension, syncope, and fracture risk in patients indicated for antihypertensive treatment: the STRATIFY models.

Nature communications·2025
Same author

The importance of experience: insights into optimal home-blood pressure monitoring regimens from the TASMINH4 Trial.

Journal of hypertension·2025
Same author

Global Blood Pressure Screening During and After Pregnancy: May Measurement Month 2019.

Hypertension (Dallas, Tex. : 1979)·2024
Same author

Statistical models for the deterioration of kidney function in a primary care population: A retrospective database analysis.

F1000Research·2022

Related Experiment Video

Updated: Sep 16, 2025

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity
08:40

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Published on: June 12, 2019

7.6K

Exploring the variation in muscle response testing accuracy through repeatability and reproducibility.

Anne M Jensen1,2,3, Richard J Stevens1,2, Amanda J Burls4

  • 1Department of Continuing Professional Education, University of Oxford, Oxford, United Kingdom.

Plos One
|July 8, 2025
PubMed
Summary
This summary is machine-generated.

Muscle response testing (MRT) accuracy varied significantly among practitioners and patients, with over 40% of variance unexplained. While statistically repeatable, clinical repeatability of MRT accuracy remains a concern.

More Related Videos

Ex Vivo Assessment of Contractility, Fatigability and Alternans in Isolated Skeletal Muscles
14:02

Ex Vivo Assessment of Contractility, Fatigability and Alternans in Isolated Skeletal Muscles

Published on: November 1, 2012

24.0K
Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
09:44

Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients

Published on: April 12, 2011

81.7K

Related Experiment Videos

Last Updated: Sep 16, 2025

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity
08:40

Isokinetic Robotic Device to Improve Test-Retest and Inter-Rater Reliability for Stretch Reflex Measurements in Stroke Patients with Spasticity

Published on: June 12, 2019

7.6K
Ex Vivo Assessment of Contractility, Fatigability and Alternans in Isolated Skeletal Muscles
14:02

Ex Vivo Assessment of Contractility, Fatigability and Alternans in Isolated Skeletal Muscles

Published on: November 1, 2012

24.0K
Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
09:44

Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients

Published on: April 12, 2011

81.7K

Area of Science:

  • Neurology
  • Diagnostic Accuracy Studies

Background:

  • Muscle response testing (MRT) is a diagnostic tool.
  • Variability in MRT accuracy may impact clinical utility.
  • Understanding factors influencing MRT accuracy is crucial.

Purpose of the Study:

  • To investigate the variability in mean muscle response testing (MRT) accuracy.
  • To determine if participant characteristics explain the observed variations in MRT accuracy.

Main Methods:

  • A prospective diagnostic test accuracy study using a round-robin design.
  • Sixteen practitioners evaluated 7 test patients using 20 MRTs.
  • Analyses included variance (ANOVA), scatterplots, and Bland-Altman plots to assess reproducibility and repeatability.

Main Results:

  • Mean MRT accuracy was 0.616, significantly higher than intuitive guessing (0.507) and chance (p<0.01).
  • Large variances in accuracy were observed among practitioners and test patients.
  • Neither practitioner nor test patient characteristics predicted MRT accuracy (p>0.19), with 43% of variance unexplained.

Conclusions:

  • MRT accuracy demonstrates significant variability, exceeding 40% unexplained variance.
  • While statistically repeatable, the wide range of scores suggests insufficient clinical repeatability.
  • Further research is needed to identify factors influencing MRT accuracy and improve its reliability.