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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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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:
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?
Halo Effect01:27

Halo Effect

The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...

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Related Experiment Video

Updated: May 27, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Influence of selection bias on the test decision. A simulation study.

M Tamm1, E Cramer, L N Kennes

  • 1RWTH Aachen University, Department of Medical Statistics, Pauwelsstraße 30, 52074 Aachen, Germany. mtamm@ukaachen.de

Methods of Information in Medicine
|November 22, 2011
PubMed
Summary

Selection bias can inflate type I error rates in unmasked randomized clinical trials, even with allocation concealment. This bias, stemming from predictable treatment assignments, can significantly impact trial results and requires careful consideration in trial design.

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Medical Research Integrity

Background:

  • Selection bias in clinical trials occurs when patients are selectively assigned to treatment groups.
  • Even with allocation concealment in randomized trials, predictable assignments can introduce selection bias.

Purpose of the Study:

  • To investigate the impact of selection bias on type I error rates in unmasked randomized trials using permuted block randomization.
  • To incorporate practical assumptions like patient characteristic misclassification for a clinically relevant error estimate.
  • To compare investigator biasing strategies and consider patient availability to establish an upper bound for type I error.

Main Methods:

  • Simulations were conducted using SAS to evaluate selection bias effects under various conditions.
  • Key factors examined included different block sizes, selection effects, biasing strategies, and patient classification success rates.
  • The study simulated practical scenarios to assess the influence on type I error rates.

Main Results:

  • Type I error rates frequently exceeded the 5% significance level, reaching up to 21%.
  • While cautious biasing strategies and misclassification could reduce, they did not eliminate selection bias.
  • The number of screened patients was approximately three times the required number for the trial.

Conclusions:

  • Selection bias significantly influences test decisions in unmasked randomized trials with permuted block randomization and allocation concealment.
  • The impact of selection bias should not be overlooked when designing and reporting clinical trials.
  • Incorporating selection bias assessment is crucial for maintaining the integrity of clinical trial evaluations.