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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...
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:
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,...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Correspondence Bias01:17

Correspondence Bias

Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the prevalence of...
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: Jun 19, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Conceptual and methodological biases in network models.

Ehud Lamm1

  • 1The Cohn Institute For the History and Philosophy of Science and Ideas, Tel Aviv University, Tel Aviv, Israel. ehudlamm@post.tau.ac.il

Annals of the New York Academy of Sciences
|October 23, 2009
PubMed
Summary

Biological network analysis reveals theoretical biases, dissolving distinctions between regulatory states and architectures. This network perspective offers insights into the evolution of development, heredity, plasticity, and epigenetic-genetic factors.

Related Experiment Videos

Last Updated: Jun 19, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Systems biology
  • Theoretical biology
  • Network science

Background:

  • Biological phenomena are increasingly modeled as complex networks.
  • Network analysis is a prevalent theoretical and empirical tool.
  • Understanding biological regulation requires examining network properties.

Purpose of the Study:

  • To discuss theoretical biases in biological network delineation.
  • To explore the evolutionary significance of regulatory network dynamics.
  • To re-evaluate fundamental biological categories through a network lens.

Main Methods:

  • Theoretical analysis of network delineation biases.
  • Conceptual framework integrating network perspective with biological concepts.
  • Exploration of evolutionary dynamics in trans-generational and interorganism networks.

Main Results:

  • The network perspective blurs the lines between regulatory architecture and state.
  • It highlights the theoretical difficulty in distinguishing 'program' from 'data' a priori.
  • The dynamics of regulatory networks have significant evolutionary implications.

Conclusions:

  • Network science provides a unified framework for understanding biological regulation.
  • Reinterpreting biological categories like development-heredity and epigenetic-genetic is facilitated by network dynamics.
  • This approach enhances our understanding of biological evolution and organization.