相关实验视频

Updated: Jul 10, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

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APS计算器:一个数据驱动的工具,用于设定基于结果的分析性能规范,用于测量不确定性,使用特定的临床要求和人口数据

Hikmet Can Çubukçu1,2, Florent Vanstapel3,4, Marc Thelen5,6

  • 1General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye.

Clinical chemistry and laboratory medicine
|November 18, 2023
PubMed
概括

实验室专业人员现在可以使用新的Web应用程序计算测量不确定性 (MU) 的分析性能规范 (APS). 该工具基于用户定义的极限和人口数据模拟了MU对患者护理结果的影响.

关键词:
这是ISO 15189的标准.分析性能规范 分析性能规范数据数据的数据数据的数据.决策限制限制限制了决策的限制.测量不确定性 测量不确定性结果结果结果结果.

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A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro11:30

A Semi-Automated and Reproducible Biological-Based Method to Quantify Calcium Deposition In Vitro

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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Significant Figures in Calculations00:58

Significant Figures in Calculations

Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:
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