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Related Concept Videos

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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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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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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
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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.