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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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...
1.4K
Random Error01:04

Random Error

8.2K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.9K
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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Random and Systematic Errors01:20

Random and Systematic Errors

11.2K
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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Random and Systematic Errors01:20

Random and Systematic Errors

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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

8.7K
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...
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Related Experiment Video

Updated: Apr 26, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Testing for ontological errors in probabilistic forecasting models of natural systems.

Warner Marzocchi1, Thomas H Jordan2

  • 1Centro per la Pericolosità Sismica, Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome, Italy; and warner.marzocchi@ingv.it tjordan@usc.edu.

Proceedings of the National Academy of Sciences of the United States of America
|August 7, 2014
PubMed
Summary

This study clarifies testing probabilistic forecasting models for errors. Severe testing, using frequentist methods and prior predictive checks, validates models for reliable predictions in natural systems.

Keywords:
Bayesian statisticsexpert opinionsignificance testingsubjective probabilitysystem science

Related Experiment Videos

Last Updated: Apr 26, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Environmental science
  • Geophysics
  • Statistics

Background:

  • Probabilistic models capture system variability and uncertainty.
  • Testing models reveals errors in representing systems and their uncertainties.

Purpose of the Study:

  • Clarify conceptual issues in testing probabilistic forecasting models for ontological errors.
  • Define a scientific pathway for capturing predictability.

Main Methods:

  • Utilize external experimental concepts for conditional exchangeability judgments.
  • Apply frequentist methods (e.g., P values) for testing ontological hypotheses.
  • Employ prior predictive model checking for more severe tests.

Main Results:

  • Testability of ontological hypotheses relies on conditional exchangeability judgments.
  • Frequentist methods can rigorously test models predicting observed behaviors.
  • Prior predictive checking offers a more severe test than posterior predictive checking.

Conclusions:

  • Severe testing under appropriate experimental concepts is crucial for model validation.
  • This methodology reliably separates predictable behaviors from unpredictable ones.
  • Validating models ensures their reliability for practical applications like seismic forecasting.