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Confirmation Biases01:31

Confirmation Biases

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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?
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Hindsight Biases01:12

Hindsight Biases

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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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Bias01:22

Bias

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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...
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Diode: Forward bias01:20

Diode: Forward bias

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In semiconductor devices, diodes play a crucial role in directing current flow, and its operation is primarily categorized into forward bias and reverse bias. A diode is said to be forward-biased when its p-type region is connected to the positive terminal of a battery and its n-type region is linked to the negative terminal. This configuration reduces the potential barrier within the diode, allowing current to flow easily from the p to the n-type region.
The behavior of a diode in forward bias...
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Biasing of FET01:22

Biasing of FET

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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...
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Biasing of P-N Junction01:16

Biasing of P-N Junction

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The operation of a p-n junction diode involves various biasing conditions, including forward bias, reverse bias, and equilibrium.
In equilibrium, no external voltage is applied across the p-n junction. The depletion region is formed at the junction interface due to the diffusion of carriers, which leaves behind charged dopants, acceptors on the p-side, and donors on the n-side. These immobile charges create an electric field that prevents further diffusion of carriers. The related energy band...
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Related Experiment Video

Updated: Jan 21, 2026

A Microfluidic Platform for Precision Small-volume Sample Processing and Its Use to Size Separate Biological Particles with an Acoustic Microdevice
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A Microfluidic Platform for Precision Small-volume Sample Processing and Its Use to Size Separate Biological Particles with an Acoustic Microdevice

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On the Bias of Precision Estimation Under Separate Sampling.

Shuilian Xie1, Ulisses M Braga-Neto1

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.

Cancer Informatics
|July 31, 2019
PubMed
Summary

Estimating classifier precision in cancer biomarker studies can be biased when using separately sampled case and control data. A modified estimator using true prevalence reduces this systematic bias.

Keywords:
Precisionbiasclassificationexperimental designobservational studyrecall

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Area of Science:

  • Biostatistics
  • Machine Learning in Healthcare
  • Cancer Research

Background:

  • Observational case-control studies for biomarker discovery often use separate sampling for cases and controls.
  • This sampling method can introduce bias in classifier performance estimation.

Purpose of the Study:

  • To analyze the bias in precision estimation for classifiers trained on separately sampled data.
  • To propose and evaluate a modified precision estimator that accounts for true population prevalence.

Main Methods:

  • Theoretical analysis of bias in precision estimation under separate sampling.
  • Numerical experiments using synthetic and real-world cancer study data.
  • Comparison of standard precision estimators with a modified estimator using known population prevalence.

Main Results:

  • Separate sampling leads to systematic bias in classifier precision estimates, independent of sample size.
  • The magnitude of bias correlates with the difference between true and sample prevalence.
  • A modified precision estimator incorporating true prevalence significantly reduces bias, especially with larger sample sizes.

Conclusions:

  • Standard precision estimation in case-control studies with separate sampling is unreliable due to systematic bias.
  • Utilizing known population prevalence in a modified estimator offers a more accurate assessment of classifier performance.
  • This finding is crucial for reliable biomarker discovery and classifier development in cancer research.