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Related Concept Videos

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.
Propagation of Uncertainty from Systematic Error01:10

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 particular...
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...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Uncertainty: Confidence Intervals00:54

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 't,' or...
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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Related Experiment Video

Updated: Jun 13, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Quantifying uncertainty and sampling quality in biomolecular simulations.

Alan Grossfield1, Daniel M Zuckerman

  • 1University of Rochester Medical Center, Department of Biochemistry and Biophysics, Box 712, Rochester, N.Y., 14642, USA, 585-276-4193.

Annual Reports in Computational Chemistry
|May 11, 2010
PubMed
Summary
This summary is machine-generated.

This review introduces statistical tools for assessing biomolecular simulation quality. It helps determine if large systems are fully sampled and if results are statistically significant, ensuring reliable simulation data.

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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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Last Updated: Jun 13, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

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

Area of Science:

  • Computational Biology
  • Statistical Mechanics
  • Biomolecular Simulations

Background:

  • Increasing computational power and algorithmic progress enable larger biomolecular system studies over longer timescales.
  • This complexity raises concerns about simulation sampling quality and statistical convergence.
  • Key questions involve system size limitations for full sampling and convergence of 'fast variables'.

Purpose of the Study:

  • To present statistical tools and physical concepts for evaluating biomolecular simulation quality.
  • To provide methods for assessing sampling completeness and statistical convergence in complex systems.
  • To guide researchers in determining the statistical significance of simulation outcomes.

Main Methods:

  • Review of established statistical analysis techniques applicable to molecular simulations.
  • Explanation of underlying physical principles governing system sampling and convergence.
  • Provision of practical definitions and ready-to-use analytical procedures.

Main Results:

  • A framework for assessing the reliability of simulation data is presented.
  • Methods are described to evaluate whether systems are adequately sampled.
  • Techniques are offered to determine convergence of dynamic variables within simulations.

Conclusions:

  • Statistical analysis is crucial for validating biomolecular simulation results.
  • The reviewed methods enhance the rigor and trustworthiness of computational studies.
  • Implementing these tools ensures the reliability of findings from large-scale simulations.