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Related Concept Videos

Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
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...
Fundamental Attribution Error01:14

Fundamental Attribution Error

According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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:

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Related Experiment Video

Updated: May 9, 2026

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

Unobserved confounders cannot explain over-crediting in avoided deforestation carbon projects.

Alejandro Guizar-Coutiño1,2, George Nicholson3, David Coomes4

  • 1Department of Plant Sciences, University of Cambridge, Cambridge, UK. alejandro.guizar-coutino@biology.ox.ac.uk.

Nature Ecology & Evolution
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

Sensitivity analyses are crucial for ecological studies using quasi-experimental designs. This research on REDD+ projects reveals over-crediting is likely, even when accounting for unobserved factors influencing deforestation and project placement.

Related Experiment Videos

Last Updated: May 9, 2026

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

Area of Science:

  • Ecology
  • Conservation Science
  • Environmental Economics

Background:

  • Quasi-experimental designs are increasingly used for causal inference in ecology and conservation.
  • Omitted variable bias from unobserved confounders is a significant risk in these studies.
  • Sensitivity analyses to assess this bias are rarely performed.

Purpose of the Study:

  • To demonstrate the value of sensitivity analyses in ecological research.
  • To investigate the overestimation of effectiveness in projects aimed at reducing emissions from deforestation and forest degradation (REDD+).
  • To assess the impact of unobserved confounders on REDD+ project evaluations.

Main Methods:

  • Revisiting a global sample of 44 REDD+ projects.
  • Employing quasi-experimental designs to analyze REDD+ project effectiveness.
  • Conducting sensitivity analyses to explore the impact of unobserved confounders.

Main Results:

  • Some REDD+ projects successfully reduced deforestation.
  • Over-crediting was prevalent across many REDD+ projects examined.
  • Sensitivity analyses indicated that unobserved local drivers are unlikely to fully explain the reported over-crediting.

Conclusions:

  • Quasi-experimental analyses of REDD+ projects may be flawed due to omitted variable bias.
  • Over-crediting in REDD+ projects is likely widespread.
  • Sensitivity analyses to unobserved confounders should become standard practice in ecological and conservation causal inference.