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

73.7K
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. 
73.7K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
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...
6.6K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

1.6K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.6K
Random and Systematic Errors01:20

Random and Systematic Errors

11.0K
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...
11.0K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

200
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%...
200

You might also read

Related Articles

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

Sort by
Same author

Real-World Actionability Analysis of Comprehensive Genomic Profiling Versus Single/Small-Gene Panels.

Oncology and therapy·2026
Same author

From rehabilitation to performance: reframing vestibular-ocular interventions to optimise sports performance.

British journal of sports medicine·2026
Same author

Dental-dedicated magnetic resonance imaging in prosthodontics: Applications, benefits, and limitations.

The Journal of prosthetic dentistry·2026
Same author

Erosive tooth wear: what are UK undergraduates being taught?

British dental journal·2026
Same author

Impacts of a Rural Hospital Global Budget Alternative Payment Model on Patterns of Cancer Surgery.

Medical care·2026
Same author

The hydrophobic behaviour of statherin is altered by pH and calcium.

Archives of oral biology·2026
Same journal

Pre-clinical evaluation of the anticaries effect of an experimental Malva sylvestris extract mouthwash using a cariogenic model in situ.

Journal of dentistry·2026
Same journal

Five-Year Outcomes of Zirconia and Fiber-Reinforced Composite Cantilever Inlay-Retained Fixed Dental Prostheses with Different Retainer Designs: A Randomized Controlled Clinical Trial.

Journal of dentistry·2026
Same journal

ACCURACY OF 2D FACIAL PROFILE PHOTOGRAPHS UNDER ROUTINE CLINICAL CONDITIONS COMPARED WITH 3D IMAGING.

Journal of dentistry·2026
Same journal

Adhesion of resin composites to 3D-printed dental resins: A study on the effect of surface conditioning methods and repair materials.

Journal of dentistry·2026
Same journal

DGADS: A Graph-based Agentic Decision Support System for Precision Dental Question Answering.

Journal of dentistry·2026
Same journal

Preventive Effects of a Strontium‑Containing Nano Bioactive Glass Hydrogel Against Enamel Caries: An In Vitro Study.

Journal of dentistry·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K

Quantifying error introduced by iterative closest point image registration.

Ningjia Sun1, Thomas Bull2, Rupert Austin1

  • 1Centre for Clinical, Oral and Translational Sciences, Faculty of Dental, Oral and Craniofacial Sciences, King's College London, Floor 17, Tower Wing, Guy's Hospital, SE1 9RT, UK.

Journal of Dentistry
|January 27, 2024
PubMed
Summary
This summary is machine-generated.

Iterative Closest Point (ICP) registration can introduce significant errors in 3D inspection, especially for small defects. A data subtraction process effectively minimizes these errors, improving measurement accuracy.

Keywords:
Dental informatics/bioinformaticsDiagnostic systemsDimensional changeImagingOral diagnosisSurface metrology software

More Related Videos

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.4K
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.0K

Related Experiment Videos

Last Updated: Jul 4, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K
Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.4K
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.0K

Area of Science:

  • Metrology and 3D inspection
  • Image registration analysis

Background:

  • 3D inspection software can introduce measurement errors, particularly for defects below 3 μm.
  • Iterative Closest Point (ICP) registration may introduce substantial errors (up to 15.63%) depending on surface complexity and defect size.

Purpose of the Study:

  • Quantify analysis errors from ICP image registration.
  • Investigate if a data subtraction process can reduce measurement errors.

Main Methods:

  • Tested metrology and 3D inspection software with calibration standards and artificial defects.
  • Assessed errors with and without ICP registration on free-form surfaces.
  • Analyzed data using ANOVA, with significance at p < 0.05.

Main Results:

  • ICP registration introduced errors from 0% to 15.63% of defect size.
  • Significant measurement differences observed between and within software, with no clear superiority.
  • Defects < 3 μm showed substantial error (13.39-77.50%) even without registration.
  • Data subtraction reduced registration errors to <1%.

Conclusions:

  • Commercial 3D inspection software introduces errors in sub-3 μm measurements.
  • ICP registration can lead to >15% error, irrespective of surface accuracy.
  • Analysis outputs are inconsistent and not comparable across software.
  • Scan subtraction significantly reduces errors but increases computational complexity.