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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.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

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...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

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.

Estimation of the Physical Quantities

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

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Related Experiment Video

Updated: Aug 11, 2025

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Strain tracking with uncertainty quantification.

Younhun Kim, Colin J Worby, Sawal Acharya

Biorxiv : the Preprint Server for Biology

|February 7, 2023

Summary

This summary is machine-generated.

ChronoStrain accurately tracks low-abundance microbial strains using sequence quality and temporal data. This new model improves strain resolution and outperforms existing methods for tracking infections and microbial communities.

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Last Updated: Aug 11, 2025

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

Magnetic Tweezers for the Measurement of Twist and Torque

Magnetic Tweezers for the Measurement of Twist and Torque

Published on: May 19, 2014

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Microbiota tracking is crucial for clinical, basic science, and public health.
Targeted and untargeted (shotgun) sequencing are key methods for microbiota analysis.
Accurate tracking of low-abundance taxa with strain-level resolution is needed, especially for pathogens.

Purpose of the Study:

To develop and validate ChronoStrain, a novel sequence quality- and time-aware model for accurate microbiota strain tracking.
To introduce uncertainty quantification for improved detection of low-abundance species.
To demonstrate ChronoStrain's superior performance over state-of-the-art methods.

Main Methods:

Developed ChronoStrain, a probabilistic model leveraging sequence quality scores and temporal sample information.
Applied ChronoStrain to real and synthetic datasets, including the UTI Microbiome (UMB) Project and Early Life Microbiota Colonisation (ELMC) Study.
Compared ChronoStrain's performance against existing strain tracking models.

Main Results:

ChronoStrain significantly outperforms current state-of-the-art methods on both real and synthetic data.
Successfully captured post-antibiotic Escherichia coli strain blooms in recurrent urinary tract infection (UTI) patients.
Accurately identified Enterococcus faecalis strains in the ELMC Study, validated by paired sample isolates.

Conclusions:

ChronoStrain provides accurate strain-level resolution for microbiota tracking, particularly for low-abundance taxa.
The model's probabilistic outputs offer insights into low-confidence and high-confidence strain assignments.
ChronoStrain represents a significant advancement in microbial strain tracking for diverse applications.