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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...
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?
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,...
Biasing of FET01:22

Biasing of FET

Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the gate...
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?

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Potential bias in GO::TermFinder.

Robert M Flight1, Peter D Wentzell

  • 1Department of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4J3, Canada.

Briefings in Bioinformatics
|March 13, 2009
PubMed
Summary
This summary is machine-generated.

Bioinformatics analysis requires accurate false discovery rate (FDR) calculations. This study identifies a sampling bias in GO::TermFinder, leading to overestimated FDR, and proposes a simple fix for more reliable results.

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Multiple statistical comparisons are common in bioinformatics, necessitating P-value adjustments.
  • The False Discovery Rate (FDR) is a widely adopted method for controlling Type I errors.
  • Accurate FDR calculation is crucial, especially with non-independent data like DNA microarrays.

Purpose of the Study:

  • To identify and quantify a bias in FDR calculation within the GO::TermFinder package.
  • To investigate the impact of incorrect random sampling on FDR estimation.
  • To propose a method for correcting the identified sampling bias.

Main Methods:

  • Utilized T(2) and permutation tests to validate findings.
  • Analyzed a test dataset to demonstrate the bias.
  • Investigated the random sampling methodology in GO::TermFinder.

Main Results:

  • Demonstrated a significant bias in FDR calculation due to incorrect random sampling in GO::TermFinder.
  • The bias resulted in an approximate 10% overestimation of the FDR.
  • Confirmed the bias using both T(2) and permutation testing.

Conclusions:

  • Incorrect random sampling in bioinformatics tools can lead to unreliable FDR estimates.
  • The GO::TermFinder package exhibits a bias leading to FDR overestimation.
  • A straightforward modification to the random sampling procedure can correct this bias, improving FDR accuracy.