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Related Concept Videos

Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Framing Effects03:26

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in different ways based on the...
Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
Motivational Bias01:25

Motivational Bias

Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...

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Related Experiment Video

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

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Published on: September 19, 2012

Biases from omitted risk effects in standard gamble utilities.

Robin Pope1

  • 1Center for European Integration Studies (ZEI), University of Bonn, Walter Flex Strasse 3, D-53113 Bonn, Germany. robin.pope@uni-bonn.de

Journal of Health Economics
|December 14, 2004
PubMed
Summary

Standard gamble procedures for health utilities can overestimate treatment benefits due to risk effects. A new approach partitions the future to better account for these biases in health economic evaluations.

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Area of Science:

  • Health economics
  • Decision theory
  • Psychometrics

Background:

  • Standard gamble (SG) is a common method for eliciting health utilities.
  • Existing SG methods may produce biased utility estimates, particularly for health interventions.

Purpose of the Study:

  • To identify and explain biases in standard gamble utility elicitation.
  • To propose a revised framework for health utility assessment.

Main Methods:

  • Analysis of biases in probability equivalence (PE) and certainty equivalence (CE) versions of the standard gamble.
  • Conceptual framework partitioning the future based on knowledge acquisition.

Main Results:

  • The PE version systematically exaggerates utility gains for health improvements.
  • The CE version introduces a reverse bias.
  • Biases stem from unaddressed pre- and post-decision risk effects.

Conclusions:

  • Standard utility elicitation methods are flawed due to anticipated risk effects.
  • Partitioning the future into distinct temporal and knowledge-based periods can improve utility assessment accuracy.