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相关概念视频

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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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.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical 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.
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...
43.3K
Data Reporting and Recording01:24

Data Reporting and Recording

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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

Data Collection I

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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...
8.0K
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...
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Data Validation01:15

Data Validation

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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.
Key parameters for method validation include:
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Updated: Jan 27, 2026

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

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重新审视大数据乐观:数据驱动的黑子算法对社会的风险

Sachit Mahajan1, Dirk Helbing1,2

  • 1Computational Social Science, ETH Zurich, Zurich, Switzerland.

Ethics and information technology
|January 26, 2026
PubMed
概括

大数据算法和科学和政策中的人工智能 (AI) 可以延续偏见和不公平. 负责任的创新需要关注系统性和参与式监督,而不仅仅是效率.

科学领域:

  • 计算机科学 计算机科学
  • 社会学 社会学 社会学
  • 公共政策 公共政策

背景情况:

  • 大数据算法和人工智能越来越多地被用于科学,社会和公共政策.
  • 这些技术旨在提高效率,但往往无法确保公平或赋权.
  • 诸如偏见,测量错误和过度依赖预测等问题可能导致不公平和不透明的结果.

研究的目的:

  • 批判性地检查大数据和人工智能的伦理风险和社会副作用.
  • 倡导从短期优化转向系统弹性和参与式监督.
  • 提出数据驱动技术负责任创新的途径.

主要方法:

  • 对大数据算法和人工智能的应用进行批判性分析.
  • 审查伦理方面的考虑,包括偏见,公平和透明度.
  • 探索社会经济影响和权力动态.

主要成果:

  • 大数据和人工智能的实施可能会加剧现有的不平等,并引入新的偏见.
  • 自动化决策可能取代人类的判断,导致公平性和透明度下降.
  • 追求纯粹的优化忽视了关键的伦理风险和社会后果.

结论:

关键词:
算法偏差是一种算法偏差.算法治理是一种算法治理.大数据就是大数据.伦理学 伦理学 伦理学负责任的创新 负责任的创新社会影响社会影响.

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  • 对大数据和人工智能的负责任创新需要关注伦理风险和社会副作用.
  • 转向"系统性"和"参与式监督"至关重要.
  • 将复杂性科学与宪法和文化价值观相结合,可以促进共生的人与技术关系.