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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...

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

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...

Uncertainty in Measurement: Accuracy and Precision

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.

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How Uncertainty Matters Under Risk Neutrality.

David Glynn¹, James Lomas²

¹Centre for Health Economics, University of York, York, England, UK.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research

|May 9, 2023

Summary

This summary is machine-generated.

Risk aversion is not always needed in cost-effectiveness analysis. Greater uncertainty can reduce technology approval likelihood for risk-neutral decision-makers due to model nonlinearities and costs.

Keywords:

decision making risk aversion risk preference uncertainty

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Area of Science:

Health Economics
Decision Analysis
Pharmacoeconomics

Background:

Cost-effectiveness analysis (CEA) often assumes risk neutrality, using expected values for decisions.
Some researchers incorporate risk aversion (RA) into CEA, believing less uncertainty is desirable.

Purpose of the Study:

To demonstrate that risk aversion is not always necessary to justify preferring less uncertain options in CEA.
To identify conditions under which greater uncertainty can reduce technology approval likelihood for risk-neutral decision-makers.

Main Methods:

Illustrative analysis within a cost-effectiveness framework.
Examination of decision-making under uncertainty with specific conditions: model nonlinearities, nonlinear opportunity costs, and irreversible costs.

Main Results:

Risk aversion is not always required to justify choosing less uncertain options.
For risk-neutral decision-makers, increased uncertainty can decrease technology approval likelihood.
Identified conditions leading to "apparent" risk aversion in CEA.

Conclusions:

Explicit risk preferences in decision-making can be complex.
Caring about uncertainty in technology approval decisions is justifiable even without explicit risk aversion.
"Apparent" risk aversion arises from specific model and cost structures.