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03:37Uncertainty 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.
02:46Uncertainty in Measurement: Reading Instruments
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
01:10Propagation 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...
00:59Propagation 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...
00:54Uncertainty: 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...
00:59Uncertainty: 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.
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Related Experiment Video
Updated: Jun 20, 2026
10:22
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
Published on: September 7, 2019
Practical postcalibration uncertainty analysis: Yucca Mountain, Nevada.
Scott C James1, John E Doherty, Al-Aziz Eddebbarh
1Thermal/Fluid Science & Engineering, PO Box 969, Livermore, CA 94551-0969, USA. scjames@sandia.gov
Ground Water
|September 12, 2009
Summary
Predictive uncertainty in groundwater flow models can be high due to scarce data. This study introduces methods to quantify uncertainty and identify key observations for improving predictive accuracy in models, such as for Yucca Mountain.
Area of Science:
- Hydrogeology
- Environmental modeling
- Risk assessment
Background:
- Groundwater flow model predictions rely on parameter calibration using historical data.
- Limited or uninformative data can lead to high predictive uncertainty, even with calibrated models.
- Accurate predictions are crucial for sites like Yucca Mountain, proposed for radioactive waste disposal.
Purpose of the Study:
- To quantitatively evaluate predictive uncertainty in groundwater flow models.
- To identify sources of uncertainty and determine the most effective observations for reducing it.
- To apply and demonstrate linear and nonlinear uncertainty analyses on a specific groundwater flow model.
Main Methods:
- Implementation of linear and nonlinear predictive error/uncertainty analyses.
- Application of these methods as an adjunct to model calibration.
- Analysis of a groundwater flow model for Yucca Mountain, Nevada.
Main Results:
- Linear analysis quantifies parameter contributions to uncertainty and the value of observations.
- Nonlinear analysis provides more accurate uncertainty characterization and approximate probability distributions.
- Uncertainty bounds for specific discharge predictions at Yucca Mountain were confirmed.
Conclusions:
- Advanced uncertainty analyses are valuable tools for groundwater model calibration.
- These methods help identify data needs to improve predictive accuracy.
- The study validates existing uncertainty bounds for the Yucca Mountain Project.