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

Introduction to Test of Independence01:21

Introduction to Test of Independence

2.4K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.4K
Randomized Experiments01:13

Randomized Experiments

7.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.9K
Bonferroni Test01:10

Bonferroni Test

2.9K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.9K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

1.8K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
1.8K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

2.1K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
2.1K
Significance Testing: Overview01:04

Significance Testing: Overview

3.8K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
3.8K

You might also read

Related Articles

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

Sort by
Same author

The I2 Statistic As Selection Bias Test: Updated Threshold Limits.

Cureus·2026
Same author

I2 Statistic as the Selection Bias Test: Trial Effect Estimates in Relation to Identified Bias Levels.

Cureus·2025
Same author

Stakeholders' Perspectives on Attrition in the Dental Therapy Profession in South Africa.

Public health challenges·2025
Same author

Perceptions on causes and effects of common oral diseases among HIV-positive and HIV-negative adults in Kigali, Rwanda: a qualitative study.

BMC oral health·2025
Same author

Deductive Falsification Instead of Inductive Verification As Logical Basis for the Critical Appraisal of Randomised Controlled Trials.

Cureus·2025
Same author

A methodological assessment of randomization integrity in alteplase for acute ischemic stroke individual patient data meta-analyses.

PloS one·2025

Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
06:13

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health

Published on: December 1, 2023

1.3K

I2 Statistic as a Test for Selection Bias in Randomised Controlled Trials.

Steffen Mickenautsch1,2, Veerasamy Yengopal1

  • 1Faculty of Dentistry, University of the Western Cape, Cape Town, ZAF.

Cureus
|June 25, 2025
PubMed
Summary
This summary is machine-generated.

The I² statistic effectively detects selection bias in randomized controlled trials (RCTs), enhancing test accuracy and identifying low-level bias. This improves the reliability of clinical trial results by preventing false positives and negatives.

Keywords:
bias identificationclinical trial appraisali2 testrandomized control trialselection biassystematic review and meta analysis

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

713
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K

Related Experiment Videos

Last Updated: Sep 18, 2025

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
06:13

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health

Published on: December 1, 2023

1.3K
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

713
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Evidence-Based Medicine

Background:

  • Selection bias can compromise the validity of randomized controlled trials (RCTs).
  • Accurate detection of selection bias is crucial for reliable clinical evidence.
  • Existing methods may not adequately identify low-level or subtle selection bias.

Purpose of the Study:

  • To demonstrate the utility of the I² statistic for testing selection bias in single RCTs.
  • To evaluate the potential of the I² statistic in preventing false-positive and false-negative results.
  • To explore the use of the I² statistic for identifying and quantifying low-level selection bias.

Main Methods:

  • Application of the I² statistic as a tool for bias assessment in RCTs.
  • Statistical analysis to evaluate test specificity and positive predictive value.
  • Exploration of the I² statistic's capability in estimating biased allocation percentages.

Main Results:

  • The I² statistic demonstrates potential for high test specificity and positive predictive value in detecting selection bias.
  • The I² statistic aids in identifying low-level selection bias, reducing false-negative results.
  • The statistic may assist in estimating the proportion of patients with biased allocation in RCTs.

Conclusions:

  • The I² statistic is a valuable tool for assessing selection bias in RCTs.
  • Its use can enhance the accuracy and reliability of clinical trial findings.
  • Further research is warranted to distinguish chance from bias and assess the impact on effect estimates.