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Related Concept Videos

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

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Related Experiment Video

Updated: May 8, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Bridging the gap between science and decision making.

Detlof von Winterfeldt1

  • 1Daniel J Epstein Department of Industrial and Systems Engineering, Viterbi School of Engineering and Sol Price School of Public Policy, University of Southern California, Los Angeles, CA 90089, USA. detlof@aol.com

Proceedings of the National Academy of Sciences of the United States of America
|August 14, 2013
PubMed
Summary
This summary is machine-generated.

Bridging science and decision-making is crucial for informed choices. This framework uses decision analysis to integrate scientific evidence with personal values for better health and policy decisions.

Keywords:
decision analysisrisk analysisrisk communicationscience communication

Related Experiment Videos

Last Updated: May 8, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

  • Decision Analysis
  • Science Communication
  • Public Policy

Background:

  • Decisions rely on beliefs and values, but science is often inaccessible to decision-makers.
  • A gap exists between scientific knowledge and the information needs of decision-makers.
  • Effective decision-making requires integrating scientific insights with individual values.

Purpose of the Study:

  • To present a framework for bridging the gap between science and decision-making.
  • To demonstrate how scientific inputs can inform personal and public decisions.
  • To illustrate the application of decision analysis in integrating scientific evidence and values.

Main Methods:

  • Developed a framework to connect scientific information with decision-maker beliefs and values.
  • Applied decision analysis to a personal health decision scenario.
  • Utilized decision analysis for a public policy environmental hazard management scenario.

Main Results:

  • The framework successfully integrated scientific inputs (e.g., physician advice) with personal values for health choices.
  • Decision analysis effectively represented controversial beliefs as uncertainties in environmental policy-making.
  • Both examples demonstrated the utility of decision analysis in bridging science and decision-making.

Conclusions:

  • A formal framework integrating science and decision-making is beneficial.
  • Decision analysis provides a robust method for combining scientific evidence with values.
  • Successful implementation requires expertise in both scientific and decision-making domains.