Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Robbers Cave04:49

Robbers Cave

During the 1950s, the landmark Robbers Cave experiment demonstrated that when groups must compete with one another, intergroup conflict, hostility, and even violence may result. At the Oklahoman summer camp, two troops of boys—termed the Rattlers and the Eagles—took part in a week-long tournament. During this time, their negativity culminated in derogatory name-calling, fistfights, and even vandalism and destruction of property. However, this work also revealed that such tension could be...
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...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
Self-Serving Bias01:29

Self-Serving Bias

Self-serving bias is a cognitive phenomenon in which individuals attribute positive outcomes to internal factors such as their abilities, intelligence, or effort while attributing negative outcomes to external circumstances. This cognitive distortion helps maintain self-esteem but can also impede objective self-assessment.Theoretical Explanations of Self-Serving BiasTwo primary theories explain the self-serving bias: the cognitive explanation and the motivational explanation.The cognitive...
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?
Mate Choice01:20

Mate Choice

Mate choice—the decision about whom to mate with—is a type of natural selection, since animals must reproduce to pass down their genes. Mate choice is also called intersexual selection because the behavior occurs between the sexes.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A prematurely terminated phase 2, randomised trial to evaluate immunogenicity and reactogenicity of a single versus two-dose primary vaccination regimen of the mRNA vaccine BNT162b2 in previously SARS-CoV-2 infected children 5-11 years old (CoVacc trial).

Vaccine·2026
Same author

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same author

On the Inclusion of Non-Concurrent Controls in Platform Trials With an Interim Analysis.

Statistics in medicine·2026
Same author

Extremity Ultrasound vs. Computed Tomography at the Third Lumbar Vertebra Level for Assessing the Subcutaneous Adipose Tissue-to-Muscle Ratio.

Nutrients·2026
Same author

Design and analysis of randomized clinical trials for onchocerciasis, loiasis and mansonellosis: A systematic review.

PLoS neglected tropical diseases·2026
Same author

Comparison of primary analysis strategies of randomized controlled trials with multiple endpoints with application to kidney transplantation.

Scientific reports·2026

Related Experiment Video

Updated: Jun 19, 2026

How to Create and Use Binocular Rivalry
14:34

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

Selection and bias--two hostile brothers.

Peter Bauer1, Franz Koenig, Werner Brannath

  • 1Section of Medical Statistics, Core Unit for Medical Statistics and Informatics, Medical University of Vienna, Vienna, Austria.

Statistics in Medicine
|October 22, 2009
PubMed
Summary
This summary is machine-generated.

This study quantifies bias and mean square error in clinical trial estimates after selecting treatments in interim analyses. Planned adaptive designs are crucial for accurately assessing bias when treatments are selected based on early data.

Related Experiment Videos

Last Updated: Jun 19, 2026

How to Create and Use Binocular Rivalry
14:34

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Clinical trials often involve multiple treatments compared to a control.
  • Interim analyses allow for the selection of the most promising treatments for further stages.

Purpose of the Study:

  • To quantify the mean bias and mean square error of conventional estimates following treatment selection in adaptive clinical trials.
  • To investigate the impact of the number of treatments and selection timing on these estimates.
  • To compare scenarios with and without sample size reassignment for selected treatments.

Main Methods:

  • Statistical modeling to estimate bias and mean square error.
  • Analysis of adaptive clinical trial designs with pre-defined selection rules.
  • Evaluation of different treatment selection timings and sample size strategies.

Main Results:

  • Mean bias patterns vary significantly based on selection rules and underlying parameter values.
  • Bias quantification is feasible in planned adaptive designs where decisions are pre-specified.
  • Sample size reshuffling influences bias and error metrics.

Conclusions:

  • Adaptive clinical trial designs with pre-defined decision rules enable accurate bias assessment.
  • Understanding bias is critical for reliable treatment selection and evaluation in multi-stage trials.
  • The choice of selection rule and sample size strategy impacts the validity of trial results.