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Related Concept Videos

Quality Control01:05

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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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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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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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...
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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...
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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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Data Quality and Data Quantity: Complements or Contradictions?

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Increasing data quantity may improve data quality, challenging the big data approach. This study explores the complex relationship between data volume and quality, considering inherent and metadata characteristics.

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Area of Science:

  • Data Science
  • Information Science
  • Health Informatics

Background:

  • Data quality is well-defined, but its relationship with data quantity is unclear.
  • The big data approach often emphasizes volume, assuming advantages over smaller, high-quality datasets.
  • Existing research lacks a comprehensive understanding of how data quantity impacts data quality.

Purpose of the Study:

  • To review the relationship between data quality and data quantity.
  • To evaluate the International Organization for Standardization (ISO) definition of data quality against data quantity aspects.
  • To explore the implications of data volume on data quality within real-world data registries.

Main Methods:

  • Confronting the ISO definition of data quality with data quantity aspects using experiences from six German registries.
  • Conducting a literature search on the combined concepts of data quality and quantity.
  • Analyzing inherent data characteristics (e.g., completeness) and metadata characteristics (e.g., breadth, depth).

Main Results:

  • Data quantity encompasses inherent characteristics like case and data completeness.
  • Data quantity also includes non-inherent metadata characteristics, such as the breadth and depth of data elements and their value sets.
  • Literature suggests an increase in data quality with greater data volume, contrary to typical big data assumptions.
  • The FAIR Guiding Principles primarily address metadata characteristics of data quantity.

Conclusions:

  • Data quantity can positively influence data quality, potentially reversing the conventional big data paradigm.
  • The current concepts of data quality and quantity do not adequately cover data usage without context, such as in data mining or machine learning.
  • Further research is needed to refine the understanding of data quantity's impact on data quality and its application in advanced analytics.