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

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.2K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.2K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
Data Collection I01:30

Data Collection I

7.9K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.9K
Data Validation01:03

Data Validation

6.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.4K
Data Validation01:15

Data Validation

732
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:
732

You might also read

Related Articles

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

Sort by
Same author

Increasing conflict between intuitions triggers deliberation.

Cognition·2026
Same author

How malicious AI swarms can threaten democracy.

Science (New York, N.Y.)·2026
Same author

Is Overconfidence a Trait? An Adversarial Collaboration.

Psychological science·2025
Same author

Persuading voters using human-artificial intelligence dialogues.

Nature·2025
Same author

Dialogues with large language models reduce conspiracy beliefs even when the AI is perceived as human.

PNAS nexus·2025
Same author

Editorial overview: The psychology of misinformation.

Current opinion in psychology·2025
Same journal

Are language models models?

The Behavioral and brain sciences·2026
Same journal

Large language models illuminate the mechanistic underpinnings of the creative aspect of language use (CALU), long regarded as a mystery.

The Behavioral and brain sciences·2026
Same journal

LLMs as a platform for studying constraint interaction: Motivation and challenges.

The Behavioral and brain sciences·2026
Same journal

Beyond the data gap: Children create languages, violate their input statistics, and exhibit critical periods.

The Behavioral and brain sciences·2026
Same journal

Not-so-strange love: Language models and generative linguistic theories are more compatible than they appear.

The Behavioral and brain sciences·2026
Same journal

Rich data drive generalization: Lessons from machine learning for linguistics and cognitive science.

The Behavioral and brain sciences·2026
See all related articles

Related Experiment Video

Updated: Jan 25, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

You are not your data.

Gordon Pennycook1

  • 1Department of Psychology,Yale University,New Haven,CT 06520-8205.gordon.pennycook@yale.edugordonpennycook.net.

The Behavioral and Brain Sciences
|May 9, 2019
PubMed
Summary
This summary is machine-generated.

Scientists must prioritize truth by separating personal identity from research data. Rewarding honest reactions to failed replications, not just findings, will promote scientific integrity and reproducibility.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.9K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.3K

Related Experiment Videos

Last Updated: Jan 25, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.9K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.3K

Area of Science:

  • Scientific integrity
  • Research methodology
  • Reproducibility in science

Background:

  • The scientific community often faces challenges with reproducibility.
  • Personal identity can become intertwined with research findings.
  • Current reward systems may inadvertently discourage reporting negative or unreproducible results.

Purpose of the Study:

  • To propose a framework for enhancing scientific truth-seeking.
  • To advocate for a shift in how scientific success is measured.
  • To encourage the normalization of replication studies.

Main Methods:

  • Conceptual analysis of scientific values.
  • Discussion of incentive structures in academia.
  • Proposal for revised metrics of scientific contribution.

Main Results:

  • Separating scientist identity from data is crucial for objective truth-seeking.
  • Rewarding positive responses to replication failures fosters a healthier scientific environment.
  • Shifting focus from findings to the stance toward truth aids reproducibility.

Conclusions:

  • Adopting a truth-centered reward system can significantly improve scientific reproducibility.
  • Scientists should cultivate a detachment from their data to uphold scientific integrity.
  • This approach promotes a more robust and reliable scientific enterprise.