Related Concept Videos
01:04Random Error
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...
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.
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.
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...
01:27Confounding in Epidemiological Studies
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
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Uncertainty and variability in historical time-weighted average exposure data.
1Battelle, Richland, WA 99352-0999, USA.
Health Physics
|January 12, 2008
Summary
This study retrospectively assessed uncertainty in historical worker exposure data from uranium and thorium processing plants. Monte Carlo methods revealed lognormal distributions for airborne contaminants and identified errors in original reports.
Area of Science:
- Occupational Health and Safety
- Environmental Science
- Nuclear Engineering
Background:
- Private companies processed uranium and thorium for the U.S. Atomic Energy Commission (AEC) starting in 1940.
- AEC's Health and Safety Laboratory (HASL) assessed worker radiation exposures using a time-and-task approach for airborne contaminants.
- HASL developed "daily weighted average" (DWA) concentrations but lacked associated uncertainties, limiting their use in dose reconstruction.
Purpose of the Study:
- To retrospectively assess uncertainty and variability in historical DWA exposure values.
- To evaluate the statistical distributions of airborne uranium, thorium, and radon decay products.
- To identify potential errors in HASL-reported DWA values.
Main Methods:
- Employed Monte Carlo simulations to analyze DWA data from 63 job titles across five processing facilities (1948-1955).
- Assessed air sample data for uranium (U), uranium ore, thorium (Th), and radium-226/radon-222 (226Ra-222Rn).
- Analyzed the distribution of DWA values, including geometric standard deviation (GSD), and identified reporting errors.
Main Results:
- Most air sample groups followed lognormal distributions.
- Combining task-specific samples often reduced the GSD of DWA values.
- A GSD of 5 is supported for DWA uncertainty when specific data is unavailable.
- Arithmetic, transposition, and transcription errors were found in HASL reports, affecting DWA values by factors of 2-10.
Conclusions:
- Historical DWA data can be statistically analyzed to provide uncertainty estimates.
- Lognormal distributions are appropriate for modeling airborne contaminant exposures in these facilities.
- Reporting errors in original HASL data can significantly impact dose reconstruction and compensation decisions.