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

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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.
Bonferroni Test01:10

Bonferroni Test

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...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Significance Testing: Overview01:04

Significance Testing: Overview

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...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

You might also read

Related Articles

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

Sort by
Same author

Predicting the Treatment Regimen Estimand in Phase 3 Studies from the Estimated Efficacy Estimand Based on Phase 2 Data.

Therapeutic innovation & regulatory science·2026
Same author

Estimating the True MACE Benefits From Tirzepatide in SURPASS-CVOT Using an Imputed Placebo Analysis of REWIND.

Diabetes care·2026
Same author

Relationships Between Metabolic and Bariatric Surgery and Adverse Kidney Outcomes: An Analysis of a Retrospective Cohort.

Obesity science & practice·2025
Same author

Bayesian Integrated Learning of Longitudinal Dose-Response Relationships via Decentralized Clinical Trials.

Statistics in medicine·2025
Same author

Estimand Endpoints for Longitudinal Measures of Continuous Disease Progression with an Alzheimer's Disease Example.

Therapeutic innovation & regulatory science·2025
Same author

Estimating risk of consequences following hypoglycaemia exposure using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

Diabetologia·2024

Related Experiment Video

Updated: Jul 6, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Evaluation of the statistical power for multiple tests: a case study.

Adeline Yeo1, Yongming Qu

  • 1Eli Lilly and Company, Indianapolis, IN 48285, USA. yeo adeline ai lin@lilly.com

Pharmaceutical Statistics
|April 3, 2008
PubMed
Summary
This summary is machine-generated.

Estimating statistical power for complex clinical trial testing strategies is difficult. This study uses the Bonferroni Inequality to provide a lower bound for statistical power, aiding in sample size determination.

More Related Videos

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Related Experiment Videos

Last Updated: Jul 6, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Accurate statistical power estimation is crucial for clinical trial design.
  • Complex testing strategies for multiple comparisons complicate power calculations.
  • Type-I error adjustment is a key consideration in clinical trials.

Purpose of the Study:

  • To develop a method for estimating the lower bound of statistical power in clinical trials with complex multiple comparison adjustments.
  • To apply the Bonferroni Inequality for statistical power estimation when test statistic correlations are unknown.
  • To aid in sample size and power calculations for clinical study designs.

Main Methods:

  • Utilized the Bonferroni Inequality to estimate the lower bound of statistical power.
  • Assumed approximate normality of test statistics.
  • Handled unknown or partially known correlation structures among test statistics.

Main Results:

  • The Bonferroni Inequality provides a feasible method for estimating the lower bound of statistical power.
  • The method is applicable even when the correlation structure of test statistics is not fully known.
  • Demonstrated application in a clinical study design for sample size and power estimation.

Conclusions:

  • The proposed method offers a practical approach to estimating statistical power in complex clinical trial scenarios.
  • This technique assists researchers in determining appropriate sample sizes for trials with multiple comparisons.
  • The Bonferroni Inequality serves as a valuable tool for robust statistical power assessment.