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Related Concept Videos

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.
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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.
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
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...

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Related Experiment Video

Updated: May 19, 2026

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

LCA data quality: sensitivity and uncertainty analysis.

M Guo1, R J Murphy

  • 1Department of Life Sciences, Imperial College of Science and Technology and Medicine, London SW7 2AZ, UK. miao.guo06@imperial.ac.uk

The Science of the Total Environment
|August 3, 2012
PubMed
Summary
This summary is machine-generated.

Life cycle assessment (LCA) data quality is crucial. Varying time horizons and statistical uncertainty analysis in LCA models enhance robustness and confidence in comparative environmental assessments of biopolymers versus petrochemicals.

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Last Updated: May 19, 2026

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

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

Area of Science:

  • Environmental Science
  • Materials Science
  • Chemical Engineering

Background:

  • Life cycle assessment (LCA) is vital for evaluating product environmental impacts.
  • Data quality and methodological choices significantly influence LCA outcomes.
  • Comparative assessments of biopolymers and petrochemicals require robust methodologies.

Purpose of the Study:

  • To investigate life cycle assessment (LCA) data quality issues using case studies.
  • To assess the impact of time horizon selection on environmental profiles.
  • To integrate statistical methods for uncertainty analysis in LCA.

Main Methods:

  • Case studies comparing starch-polyvinyl alcohol biopolymers with petrochemical alternatives.
  • Sensitivity analysis of time horizon parameter in characterization models.
  • Calibration of probabilities for LCA outcomes using statistical methods to address inventory uncertainty and data variation.

Main Results:

  • The time horizon is a sensitive parameter affecting environmental profiles, altering comparisons between biopolymers and petrochemicals.
  • Dynamic LCA characterization models with varying time horizons are recommended for robust comparative assessments.
  • Statistical integration improved confidence in LCA findings by quantifying uncertainty.

Conclusions:

  • LCAs must explicitly address uncertainty and sensitivity for reliable decision-making.
  • Dynamic LCA models and statistical uncertainty analysis are essential for accurate environmental profiling.
  • Robust LCA methodologies are critical for valid comparative assertions between material types.