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

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

8.5K
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...
8.5K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

7.7K
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...
7.7K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.7K
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...
6.7K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

274
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
274
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.6K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.6K
Quality Assurance01:19

Quality Assurance

867
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
867

You might also read

Related Articles

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

Sort by
Same author

Academic research values: Conceptualization and initial steps of scale development.

PloS one·2025
Same author

Questionable research practices in competitive grant funding: A survey.

PloS one·2023
Same author

Leaving academia: PhD attrition and unhealthy research environments.

PloS one·2022
Same author

Reply to McGrew: Chimpanzees do not exhibit widespread cultural diffusion.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

Middle Pleistocene fire use: The first signal of widespread cultural diffusion in human evolution.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

A new framework for teaching scientific reasoning to students from application-oriented sciences.

European journal for philosophy of science·2021

Related Experiment Video

Updated: Dec 12, 2025

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
05:17

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing

Published on: October 10, 2025

212

How will we find the elephant in the room?

Wybo Houkes1, Krist Vaesen1

  • 1Philosophy & Ethics, Eindhoven University of Technology, Eindhoven, The Netherlands.w.n.houkes@tue.nl k.vaesen@tue.nl.

The Behavioral and Brain Sciences
|August 11, 2020
PubMed
Summary

Osirak and Reynaud's technological-reasoning hypothesis faces challenges. Its concepts and methods are unclear, making empirical differentiation difficult due to intertwined technical potential and expertise.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K

Related Experiment Videos

Last Updated: Dec 12, 2025

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
05:17

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing

Published on: October 10, 2025

212
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.8K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K

Area of Science:

  • Social Sciences
  • Technology Studies
  • Philosophy of Science

Background:

  • The technological-reasoning hypothesis by Osirak and Reynaud proposes a framework for understanding technology adoption and development.
  • Existing research has not fully addressed the conceptual clarity and empirical testability of this hypothesis.

Purpose of the Study:

  • To critically evaluate the conceptual and methodological underpinnings of Osirak and Reynaud's technological-reasoning hypothesis.
  • To identify ambiguities in the hypothesis related to the interplay of technical potential and expertise.

Main Methods:

  • Conceptual analysis of the technological-reasoning hypothesis.
  • Methodological critique of empirical differentiation strategies.

Main Results:

  • The technological-reasoning hypothesis presents significant conceptual challenges.
  • The interrelation between technical potential and expertise creates ambiguity, obscuring the hypothesis's precise scope.
  • The hypothesis is compatible with multiple, empirically indistinguishable alternative hypotheses.

Conclusions:

  • The technological-reasoning hypothesis requires refinement to enhance its conceptual clarity and empirical testability.
  • Further research is needed to develop methods capable of differentiating the hypothesis from related theoretical constructs.