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Related Concept Videos

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...
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...
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:
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...
Actor-Observer Effect01:23

Actor-Observer Effect

The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in visual...
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?

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Related Experiment Video

Updated: May 23, 2026

Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance
13:20

Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance

Published on: December 5, 2025

[Bias in observational research: 'confounding'].

Rolf H H Groenwold1

  • 1Julius Centrum voor Gezondheidswetenschappen en Eerstelijns Gezondheidszorg, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands. r.h.h.groenwold@umcutrecht.nl

Nederlands Tijdschrift Voor Geneeskunde
|March 30, 2012
PubMed
Summary
This summary is machine-generated.

Confounding in observational studies distorts results when a third factor influences both the exposure and outcome. Researchers are developing methods to address unmeasured confounders and improve causal inference in pharmaceutical and exposure research.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Observational Research Methods

Background:

  • Confounding is a prevalent challenge in observational research, particularly for pharmaceutical effects and etiologic factor exposure.
  • It occurs when an unmeasured third factor is associated with both the determinant (exposure) and the outcome, distorting the true causal relationship.

Purpose of the Study:

  • To discuss the issue of confounding in observational studies.
  • To outline common methods for controlling confounding.
  • To highlight current research directions for addressing unmeasured confounding.

Main Methods:

  • Discusses restriction, stratification, multivariable regression models, and propensity score methods for controlling measured confounders.
  • Highlights the limitation of controlling only for measured confounders.
  • Mentions ongoing research into instrumental variables and time-dependent confounding.

Main Results:

  • Standard methods effectively control for measured confounders.
  • A significant limitation exists in addressing unmeasured confounding.
  • Current research focuses on advanced techniques to mitigate unmeasured confounding's impact.

Conclusions:

  • Effective control of measured confounding is achievable with established statistical methods.
  • Addressing unmeasured confounding remains a critical area for improving the validity of observational research.
  • Future research directions aim to enhance causal inference by incorporating external knowledge and evaluating novel methods.