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

False Memories01:18

False Memories

672
False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
One primary source of false memories is misattribution, where individuals incorrectly associate external information...
672
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

707
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
707
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.8K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.8K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.4K
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.
6.4K
Bias01:22

Bias

8.0K
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...
8.0K
Statistical Significance01:37

Statistical Significance

24.4K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
24.4K

You might also read

Related Articles

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

Sort by
Same author

Prediction models in clinical guidelines: a scoping review of clinical guideline guidance development documents.

BMJ evidence-based medicine·2026
Same author

A Knowledge Base of Designs and Statistical Methods for Adaptive Clinical Dose-Finding Trials.

Pharmaceutical statistics·2026
Same author

Available guidance for ethical challenges in learning health systems: an integrative literature review.

Health research policy and systems·2026
Same author

Missing confounding information in counterfactual prediction models: a simulation study on model-based treatment effect evaluation in radiotherapy techniques.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026
Same author

The inclusion and intended use of prediction models in clinical guidelines: a systematic review of five clinical domains in four countries.

Journal of clinical epidemiology·2026
Same author

Intrauterine human chorionic gonadotropin administration before embryo transfer (IHABT): an individual participant data meta-analysis of randomized controlled trials.

Human reproduction update·2026

Related Experiment Video

Updated: Apr 14, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

79.6K

Meta-analyses triggered by previous (false-)significant findings: problems and solutions.

Ewoud Schuit1,2,3, Kit C B Roes4, Ben W J Mol5

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands. eschuit@stanford.edu.

Systematic Reviews
|April 25, 2015
PubMed
Summary
This summary is machine-generated.

Including studies with false-significant findings in meta-analyses inflates results. A bias-correction method can adjust for these false-significant effects, ensuring accurate meta-analysis outcomes.

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.4K
A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
09:52

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

1.5K

Related Experiment Videos

Last Updated: Apr 14, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

79.6K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.4K
A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
09:52

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

1.5K

Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Meta-analyses are often initiated by potentially false-significant findings in primary studies.
  • This study investigates the impact of including such triggering primary studies in meta-analyses.

Purpose of the Study:

  • To analytically determine the bias in treatment effect estimates from meta-analyses.
  • To assess the type I error rate and power of meta-analyses including false-significant studies.
  • To evaluate a bias-correction method for meta-analytical estimates.

Main Methods:

  • Analytical determination of bias based on the number of included primary and false-significant studies.
  • Simulations used to assess type I error rates and statistical power.
  • Application of a bias-correction technique by subtracting derived bias from meta-analysis effect estimates.

Main Results:

  • Bias and type I error rates in meta-analyses increase with the proportion of included false-significant studies.
  • With 20% false-significant studies, bias was 0.33 (z-score) and type I error rate reached 23%.
  • Bias correction successfully restored the type I error rate to the expected 5%.

Conclusions:

  • False-significant findings in primary studies introduce bias and inflate type I error rates in meta-analyses.
  • The magnitude of bias is dependent on the number of false-significant studies included.
  • A practical bias-correction method is available to adjust for these inaccuracies.