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

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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% chance...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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...
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
Data Collection by Survey01:07

Data Collection by Survey

The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...

You might also read

Related Articles

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

Sort by
Same journal

Algorithm dependence of patient phenotypes in Long COVID: a patient-led, multi-method clustering of 6031 patients using 162 self-reported symptoms.

Oxford open immunology·2026
Same journal

A new patient-led approach to building research infrastructure and evidence generation.

Oxford open immunology·2026
Same journal

Enhancing protein immunogenicity prediction via uncertainty weighted deep ensemble.

Oxford open immunology·2026
Same journal

Incidence age is bimodal for myalgic encephalomyelitis/chronic fatigue syndrome, with higher severity burden for early onset disease.

Oxford open immunology·2026
Same journal

Correction to: Scientific communication and vaccine hesitation: an analysis of the editorial line of a great Brazilian newspaper.

Oxford open immunology·2026
Same journal

ImmUQBench: a benchmark on uncertainty quantification of protein immunogenicity prediction.

Oxford open immunology·2026
See all related articles

Related Experiment Video

Updated: May 15, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Correction to: 9 papers to add missing data availability statements

    Oxford Open Immunology
    |May 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study corrects previously published article DOIs. These corrections ensure accurate citation and retrieval of scientific literature, improving research integrity.

    More Related Videos

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    Related Experiment Videos

    Last Updated: May 15, 2026

    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
    07:50

    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

    Published on: September 20, 2018

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
    09:43

    Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

    Published on: November 22, 2019

    Area of Science:

    • Scientific Publishing
    • Bibliometrics
    • Scholarly Communication

    Background:

    • Accurate Digital Object Identifiers (DOIs) are crucial for the reliable retrieval of scientific publications.
    • Errors in DOIs can hinder access to research and impact citation tracking.
    • Maintaining the integrity of the scientific record is essential for academic progress.

    Purpose of the Study:

    • To correct and update Digital Object Identifiers (DOIs) for several published articles.
    • To ensure accurate referencing and accessibility of the scientific literature.
    • To uphold the standards of scholarly communication and research integrity.

    Main Methods:

    • Identification of articles with erroneous DOIs.
    • Verification of correct DOI information through database cross-referencing.
    • Formal submission of corrections to the relevant publishing platforms.

    Main Results:

    • A series of specific article DOIs have been corrected.
    • The corrected DOIs are now accurately linked to their respective publications.
    • This process enhances the discoverability and citation accuracy of the affected research.

    Conclusions:

    • The timely correction of DOI errors is vital for the scientific community.
    • Accurate metadata ensures the long-term accessibility and impact of research.
    • Adherence to publishing standards supports robust scholarly communication.