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Related Concept Videos

How Data are Classified: Numerical Data00:59

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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.
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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.
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Data Reporting and Recording01:24

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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...
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Data Collection I01:30

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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...
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Data Validation01:03

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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.
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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.
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Updated: Jan 22, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
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Why we need a small data paradigm.

Eric B Hekler1, Predrag Klasnja2, Guillaume Chevance3

  • 1Center for Wireless & Population Health Systems, Department of Family Medicine and Public Health, Design Lab and Qualcomm Institute Faculty Member, UC San Diego, 9500 Gilman Ave, San Diego, CA, 92093, USA. ehekler@eng.ucsd.edu.

BMC Medicine
|July 18, 2019
PubMed
Summary
This summary is machine-generated.

A complementary "small data" approach is crucial for achieving personalized medicine. This method uses data from specific individuals or units to improve health outcomes, working alongside "big data" for comprehensive precision health.

Keywords:
Artificial intelligenceData sciencePersonalized medicinePrecision healthPrecision medicineSmall data

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Area of Science:

  • Precision Health
  • Data Science
  • Biomedical Informatics

Background:

  • Personalized medicine holds great promise, but

Purpose of the Study:

  • Articulate the necessity and value of a small data paradigm in precision health.
  • Outline future research directions for small data study designs and analytics.
  • Highlight the complementary role of small data alongside big data initiatives.

Main Methods:

  • Defining "small data" as rigorous, N-of-1 unit-specific data utilization.
  • Leveraging small data for improved individual-level description, prediction, and control.
  • Developing methods for integrating small data into practice and combining it with big data.

Main Results:

  • Small data uniquely manages complex, dynamic, and multi-causal health phenomena like chronic diseases.
  • Small data approaches enable more rapid, agile learning by aligning science and practice goals.
  • Small data offers a pathway to transportable knowledge complementary to big data insights.

Conclusions:

  • Small data possesses inherent value for precision health.
  • Combining small and big data paradigms, underpinned by causal science, is essential for achieving precision health.
  • Integrated approaches promise to fully realize the vision of personalized and precision medicine.