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Related Concept Videos

Quality Assurance01:19

Quality Assurance

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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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Statistical Analysis: Overview01:11

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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 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.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Accuracy and Precision01:52

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Data Validation01:15

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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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Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research
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Is it possible to measure good science?

Andrew N Holding1, Kirsty R McIntyre2, Paul T Lynch3

  • 1Department of Biology, University of York, York, YO10 5DD, UK.

The FEBS Journal
|November 30, 2022
PubMed
Summary
This summary is machine-generated.

Research metrics present challenges for fair funding. However, initiatives like SF-DORA offer opportunities to improve evaluation systems, especially for Early Career Lecturers (ECLs), fostering optimism.

Keywords:
COVID-19early careerequityfundingimpactmetricsscience

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Area of Science:

  • Research evaluation
  • Scientific funding
  • Academic assessment

Background:

  • Metrics are crucial for research valuation and funding globally.
  • Current metrics face challenges in ensuring fairness and equity in research funding.
  • The impact of metric choice on Early Career Lecturers (ECLs) requires specific attention.

Approach:

  • Reviewing the inherent challenges of research metrics in funding.
  • Highlighting reform attempts, such as the San Francisco Declaration on Research Assessment (SF-DORA).
  • Analyzing the specific effects of metric selection on ECL evaluation and career progression.

Key Points:

  • Metrics often fall short of providing a completely satisfactory evaluation system.
  • The San Francisco Declaration on Research Assessment (SF-DORA) aims to improve the research environment.
  • Metric selection significantly influences the assessment and advancement opportunities for ECLs.

Conclusions:

  • Despite limitations, opportunities exist to enhance the research environment for ECLs.
  • There is a basis for optimism regarding the future of research evaluation and funding.
  • Improving metric usage can lead to a more equitable system for researchers worldwide.