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Related Concept Videos

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.
Propagation of Uncertainty from Random Error00:59

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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
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...
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...
Significant Figures in Calculations00:58

Significant Figures in Calculations

Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:

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Related Experiment Video

Updated: Jun 14, 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

Practical issues in handling data input and uncertainty in a budget impact analysis.

M J C Nuijten1, T Mittendorf, U Persson

  • 1Institute for Medical Technology Assessment (IMTA), Erasmus University Rotterdam, PO Box 1738, Rotterdam, 3000 DR, Rotterdam, The Netherlands. nuijten@bmg.eur.nl

The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care
|April 6, 2010
PubMed
Summary

Budget impact analysis (BIA) requires careful handling of uncertainty. Standard sensitivity analyses may be limited for BIA data, suggesting scenario analyses are often more appropriate for comprehensive uncertainty assessment.

Related Experiment Videos

Last Updated: Jun 14, 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

Area of Science:

  • Health Economics
  • Pharmacoeconomics
  • Decision Science

Background:

  • Uncertainty is a critical component in budget impact analysis (BIA).
  • Standard health economic models often use sensitivity analyses for uncertainty.
  • BIA models present unique challenges for uncertainty quantification.

Purpose of the Study:

  • To explore the systematic and comprehensive management of uncertainty in budget impact analysis.
  • To compare uncertainty handling in health economics with that in BIA.
  • To identify appropriate methods for addressing uncertainty in BIA.

Main Methods:

  • Literature review and conceptual overview.
  • Comparison of data sources and methods used in health economic models versus BIA.
  • Analysis of limitations of standard sensitivity analyses for BIA.

Main Results:

  • BIA models frequently rely heavily on Delphi panels and include forecasts (e.g., growth, uptake, substitution).
  • Standard sensitivity analyses may be insufficient due to limited data distributions and forecasting needs in BIA.
  • Scenario analyses are often better suited for capturing uncertainty in BIA.

Conclusions:

  • Systematic and comprehensive uncertainty assessment is crucial for robust BIA.
  • The unique data characteristics of BIA necessitate alternative approaches to uncertainty analysis.
  • Scenario analysis is recommended as a more appropriate method for BIA uncertainty.