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

2.1K
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...
2.1K
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

2.1K
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...
2.1K
Random and Systematic Errors01:20

Random and Systematic Errors

12.1K
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...
12.1K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.4K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

271
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%...
271
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.4K
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.
4.4K

You might also read

Related Articles

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

Sort by
Same author

Scaling and foraging behaviour drive the evolution of humeral shape in hummingbirds.

Proceedings. Biological sciences·2026
Same author

From policy to practice: progress towards data- and code-sharing in ecology and evolution.

Proceedings. Biological sciences·2025
Same author

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology.

BMC biology·2025
Same author

Estimating maximum oxygen uptake of fishes during swimming and following exhaustive chase - different results, biological bases and applications.

The Journal of experimental biology·2024
Same author

Reply to: Recognizing and marshalling the pre-publication error correction potential of open data for more reproducible science.

Nature ecology & evolution·2023
Same author

Kinematics and behaviour in fish escape responses: guidelines for conducting, analysing and reporting experiments.

The Journal of experimental biology·2023

Related Experiment Video

Updated: Aug 28, 2025

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

9.6K

No evidence that mandatory open data policies increase error correction.

Ilias Berberi1, Dominique G Roche2,3

  • 1Department of Biology, Carleton University, Ottawa, Canada. iliasberberi@gmail.com.

Nature Ecology & Evolution
|September 15, 2022
PubMed
Summary
This summary is machine-generated.

Open data policies in ecology journals do not appear to improve scientific accuracy. Poor data archiving, lack of open code, and stigma surrounding corrections hinder science self-correction despite data sharing requirements.

More Related Videos

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

31.9K
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

16.0K

Related Experiment Videos

Last Updated: Aug 28, 2025

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

9.6K
Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

31.9K
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

16.0K

Area of Science:

  • Ecology
  • Evolutionary Biology
  • Scientific Publishing

Background:

  • Open data policies are increasingly adopted by scientific journals.
  • The potential for open data to enhance scientific reproducibility and error detection is widely discussed.

Purpose of the Study:

  • To investigate the relationship between journal data sharing requirements and scientific article retractions or corrections.
  • To identify factors influencing the effectiveness of open data in promoting scientific self-correction.

Main Methods:

  • Analysis of open data policies across 199 journals in ecology and evolution.
  • Examination of correlations between data sharing mandates and rates of article retractions or corrections.

Main Results:

  • No statistically significant link was found between journal data sharing requirements and the frequency of article retractions or corrections.
  • Factors such as poor data archiving, lack of accessible code, and the stigma associated with scientific corrections may impede the error-detection benefits of open data.

Conclusions:

  • Current open data policies alone may not be sufficient to improve scientific self-correction in ecology and evolution.
  • Enhancing the effectiveness of open data requires addressing issues of data/code accessibility and fostering a culture that destigmatizes scientific error correction.