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Uncertainty: Overview

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.

Uncertainty: Confidence Intervals

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...

Propagation of Uncertainty from Random Error

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...

Uncertainty in Measurement: Significant Figures

Uncertainty in Measurement: Significant Figures

All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.

Propagation of Uncertainty from Systematic Error

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...

Production Efficiency

Production Efficiency

Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).

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Related Experiment Video

Updated: Nov 24, 2025

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

Ranking negative emissions technologies under uncertainty.

W Y Ng¹, C X Low¹, Z A Putra¹

¹Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia.

|December 28, 2020

Summary

This summary is machine-generated.

Negative emission technologies (NETs) are crucial for meeting Paris Agreement goals. Bioenergy with Carbon Capture and Storage (BECCS) is the most effective NET, with energy requirements being a key deployment factor.

Keywords:

Chemical engineering Decision analysis Environmental science Fuzzy analytic hierarchy process Negative emission technologies Technique for order preference by similarity to ideal solution Uncertainty

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Last Updated: Nov 24, 2025

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

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Published on: December 6, 2018

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Area of Science:

Environmental Science
Climate Change Mitigation
Decision Analysis

Background:

Current greenhouse gas (GHG) mitigation strategies are insufficient to meet Paris Agreement targets.
Deployment of negative emission technologies (NETs) is essential for achieving climate goals.
Evaluating and prioritizing NETs faces challenges due to multiple criteria and data uncertainty.

Purpose of the Study:

To develop and apply a systematic method for ranking and prioritizing NETs.
To address the complexities of multi-criteria evaluation and techno-economic uncertainties in NET assessment.
To identify the most viable NETs for achieving negative emission goals.

Main Methods:

An integrated model combining fuzzy Analytical Hierarchy Process (AHP) and interval-extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was developed.
The hybrid model was applied to assess the potential of various NETs.
Sensitivity analysis was performed to ensure the robustness of the NET rankings.

Main Results:

Bioenergy with Carbon Capture and Storage (BECCS) was identified as the most optimal NET.
BECCS demonstrated robust performance across the evaluated criteria.
Energy requirement was identified as the most critical criterion for NET deployment.

Conclusions:

The proposed hybrid fuzzy AHP and interval-extended TOPSIS model provides a robust framework for evaluating NETs.
BECCS is a highly promising technology for achieving negative emission targets.
The study offers a novel perspective for NET viability assessment and extends decision-making model applications.