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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 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...
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...
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.
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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Related Experiment Video

Updated: May 22, 2026

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

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Published on: September 17, 2021

Quantifying the Uncertainty of Molecular Dynamics Simulations: Good-Turing Statistics Revisited.

Vasiliki Tsampazi1, Nicholas M Glykos1

  • 1Department of Molecular Biology and Genetics, Democritus University of Thrace, University Campus, Alexandroupolis, Greece.

Journal of Computational Chemistry
|May 21, 2026
PubMed
Summary

A new Good-Turing algorithm variant efficiently estimates novel biomolecular structures from molecular dynamics (MD) trajectories. This method overcomes previous memory limitations, enabling analysis of extremely long simulations.

Keywords:
biomolecular simulationconvergencemolecular dynamics simulationprotein folding simulationssufficient samplinguncertainty of simulations

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Area of Science:

  • Computational Biology
  • Biophysics
  • Statistical Mechanics

Background:

  • Good-Turing statistics can predict unobserved biomolecular structures from molecular dynamics (MD) trajectories.
  • Previous implementations required significant memory for RMSD matrix calculations, limiting analysis of long simulations.

Purpose of the Study:

  • To develop a memory-efficient variant of the Good-Turing algorithm for analyzing MD trajectories.
  • To enable the application of Good-Turing statistics to extremely long molecular dynamics simulations.

Main Methods:

  • Developed a novel Good-Turing algorithm variant with linear memory scaling.
  • Applied the algorithm to molecular dynamics trajectories with up to 22 million structures.

Main Results:

  • The new algorithm achieves linear memory complexity, overcoming previous limitations.
  • Results from the new method are consistent with the older, memory-intensive implementation.
  • Successfully analyzed molecular dynamics trajectories containing millions of structures.

Conclusions:

  • The enhanced Good-Turing algorithm provides a scalable and efficient method for analyzing large molecular dynamics datasets.
  • This advancement facilitates the study of rare events and unobserved structures in complex biomolecular systems.