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: Categorical Data01:11

How Data are Classified: Categorical Data

45.5K
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...
45.5K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.6K
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...
38.6K
Data Reporting and Recording01:24

Data Reporting and Recording

5.5K
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...
5.5K
Data Validation01:15

Data Validation

2.4K
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:
2.4K
Data Validation01:03

Data Validation

7.0K
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...
7.0K
Data Collection II01:29

Data Collection II

10.3K
The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
10.3K

You might also read

Related Articles

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

Sort by
Same author

Cognitive biases as Bayesian probability weighting in context.

Frontiers in psychology·2025
Same author

Associations between neuropsychological profile and regional brain FDG uptake in progressive supranuclear palsy.

Journal of Parkinson's disease·2025
Same author

Post-stroke lesion correlates of errors in verbal and spatial production tasks.

Frontiers in psychology·2025
Same author

Subthalamic nucleus dynamics during executive functioning: Insights from local field potentials in Parkinson's disease.

Neuroscience·2025
Same author

Spasmodic dysphonia: the need for a combined neurological and phoniatric approach.

Journal of neural transmission (Vienna, Austria : 1996)·2024
Same author

Cognition in patients with myelin oligodendrocyte glycoprotein antibody-associated disease: a prospective, longitudinal, multicentre study of 113 patients (CogniMOG-Study).

Journal of neurology, neurosurgery, and psychiatry·2024

Related Experiment Video

Updated: Feb 15, 2026

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
08:33

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

13.0K

Data quality over data quantity in computational cognitive neuroscience.

Antonio Kolossa1, Bruno Kopp1

  • 1Department of Neurology, Hannover Medical School, Hannover, Germany.

Neuroimage
|January 14, 2018
PubMed
Summary
This summary is machine-generated.

Data quality, not quantity, is crucial for computational cognitive neuroscience (CCN). Increasing data per subject significantly improves model identifiability, unlike increasing the number of subjects. This highlights the need to integrate statistics and measurement theory.

Keywords:
Computational modelingFunctional brain imagingReliabilityReplicabilitySignal-to-noise ratio

More Related Videos

Perspectives on Neuroscience
26:41

Perspectives on Neuroscience

Published on: July 31, 2007

5.4K
Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

2.8K

Related Experiment Videos

Last Updated: Feb 15, 2026

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
08:33

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

13.0K
Perspectives on Neuroscience
26:41

Perspectives on Neuroscience

Published on: July 31, 2007

5.4K
Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

2.8K

Area of Science:

  • Computational Cognitive Neuroscience
  • Neuroscience
  • Cognitive Science
  • Psychology
  • Statistics

Background:

  • Advancement in computational cognitive neuroscience (CCN) may be hindered by a prevailing statistical mindset.
  • This mindset prioritizes statistical power theory and replicability, focusing on sampling error over measurement error.
  • Current practices in behavioral and neural sciences may inadequately address measurement error.

Purpose of the Study:

  • To analyze factors impeding the progress of computational cognitive neuroscience.
  • To contrast the impact of data quantity (sampling error) versus data quality (measurement error) on model identifiability.
  • To advocate for the integration of statistics and measurement theory in CCN research.

Main Methods:

  • A simulated Bayesian model identifiability study was conducted.
  • Data quantity was manipulated by varying the number of subjects.
  • Data quality was manipulated by varying the number of data points per subject.

Main Results:

  • Varying the number of subjects had inconsequential effects on model identifiability, regardless of signal-to-noise ratios.
  • Increasing the number of data points per subject significantly improved model identifiability.
  • Data quality demonstrated a far greater impact on model identifiability than data quantity.

Conclusions:

  • Data quality is paramount over data quantity in computational cognitive neuroscience.
  • The findings underscore the critical role of measurement error in model identifiability.
  • There is a pressing need to integrate statistical and measurement theories for robust CCN research.