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

Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

4.1K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
4.1K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.9K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.9K
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

13.1K
The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
13.1K
Critical Values01:31

Critical Values

9.7K
A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
9.7K
Bonferroni Test01:10

Bonferroni Test

3.2K
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...
3.2K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.0K
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...
5.0K

You might also read

Related Articles

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

Sort by
Same author

A Phase II Trial of an Extended-Release siRNA Implant Targeting KRASG12D/V in Locally Advanced Pancreatic Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Incorporating the sample correlation into the testing of two endpoints in clinical trials.

Journal of biopharmaceutical statistics·2021
See all related articles

Related Experiment Video

Updated: Dec 26, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.0K

Exact critical values for group sequential designs with small sample sizes.

Dror M Rom1, Jaclyn A McTague1

  • 1Department of Statistics, Logecal Data Analytics , Broomall, Pennsylvania, USA.

Journal of Biopharmaceutical Statistics
|March 11, 2020
PubMed
Summary

Group sequential clinical trial designs can now use exact critical values for any sample size, improving type-1 error control. This method ensures accurate hypothesis testing, especially in small sample clinical trials.

Keywords:
Group sequential designsinterim analysestype-1 error control

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

485
The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.9K

Related Experiment Videos

Last Updated: Dec 26, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.0K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

485
The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.9K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Group sequential designs enable early stopping of clinical trials.
  • Current methods rely on large sample assumptions, potentially inflating type-1 error rates with small sample sizes.
  • Existing solutions for small samples use simulations or ad-hoc adjustments.

Purpose of the Study:

  • To develop exact joint distributions for test statistics in group sequential designs, applicable to any sample size.
  • To derive accurate critical values for common alpha-spending functions.
  • To compare the type-1 error rates of the new exact methods against traditional and existing small-sample approaches.

Main Methods:

  • Derivation of the exact joint distribution of test statistics for group sequential trials.
  • Calculation of exact critical values matching O'Brien-Fleming and Pocock alpha-spending functions.
  • Comparative analysis of type-1 error rates using exact, asymptotic, and alternative small-sample methods.

Main Results:

  • The study provides a method for calculating exact critical values for group sequential designs, valid for all sample sizes.
  • The proposed exact critical values maintain the desired type-1 error rate, unlike asymptotic methods with small samples.
  • The new approach offers improved accuracy compared to existing small-sample adjustments.

Conclusions:

  • Exact critical values provide a statistically rigorous approach for group sequential clinical trials, particularly with small sample sizes.
  • This methodology corrects the type-1 error inflation observed with traditional methods in small samples.
  • The findings support the adoption of exact methods for more reliable clinical trial outcomes.