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Uncertainty: Overview

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.

Propagation of Uncertainty from Random Error

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...

Variability: Analysis

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

Uncertainty in Measurement: Accuracy and Precision

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.

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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Related Experiment Video

Updated: Sep 16, 2025

Tail Vein Transection Bleeding Model in Fully Anesthetized Hemophilia A Mice

Tail Vein Transection Bleeding Model in Fully Anesthetized Hemophilia A Mice

Published on: September 30, 2021

Quantifying Experimental Variability in Shear-Induced Hemolysis to Support Uncertainty-Aware Hemolysis Models.

Christopher Blum¹, Markus Mous¹, Ulrich Steinseifer¹

¹Department of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany.

Annals of Biomedical Engineering

|July 4, 2025

Summary

This summary is machine-generated.

Intra-donor variability significantly impacts hemolysis measurements, exceeding differences between donors. Increasing sample size enhances measurement precision for reliable hemocompatibility testing.

Keywords:

Experimental hemolysis Inter-variability Intra-variability Uncertainty quantification

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Area of Science:

Biomedical Engineering
Hematology
Medical Device Design

Background:

Hemolysis models require experimental data for parameter fitting.
Existing experiments often lack sufficient replicates, neglecting variability.
This leads to oversimplified models and inaccurate hemolysis predictions.

Purpose of the Study:

Quantify intra- and inter-donor variability in hemolysis at fixed conditions.
Assess the impact of sample size on hemolysis measurement precision.
Improve the reliability of hemolysis models and in silico predictions.

Main Methods:

Human blood from five donors subjected to standardized shear stress and exposure time.
20 independent hemolysis index (HI) measurements per donor.
Bootstrap analysis to evaluate sample size effects on confidence intervals.

Main Results:

Intra-donor variability was four times greater than inter-donor variability.
Increased sample size (2 to 20 replicates) significantly reduced confidence intervals.
Small sample sizes may underestimate true hemolysis variability.

Conclusions:

Intra-donor variability is a major source of uncertainty in hemolysis measurements.
Higher replicate numbers are crucial for robust hemolysis estimates.
Incorporate these findings into experimental design and models for improved hemocompatibility assessment.