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

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.
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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% chance...
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...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

You might also read

Related Articles

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

Sort by
Same author

Sample Size Determination for Decision-centered Pragmatic Trials.

Journal of clinical epidemiology·2026
Same author

Linking Minimally Important Differences (MID) and Acceptable Regret to Elicit Values and Preferences in Health Decision Models.

Journal of evaluation in clinical practice·2026
Same author

How to determine the optimal duration of anticoagulation for VTE: an evidence-based decision-analytical approach.

Blood advances·2026
Same author

GRADE Guidance: Update on Developing Good Practice Statements in Guidelines.

Annals of internal medicine·2026
Same author

Theory of clinical therapeutic progress: reconciling equipoise with fat-tailed (skewed) outcomes.

Journal of clinical epidemiology·2025
Same author

Converting Evidence-Based Summary of Findings Evidence Tables Into Decision Analytical, Quality Adjusted Life Years (QALY) and Life Expectancies Metrics: A Tutorial.

Journal of evaluation in clinical practice·2025

Related Experiment Video

Updated: May 11, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Optimal type I and type II error pairs when the available sample size is fixed.

John P A Ioannidis1, Iztok Hozo, Benjamin Djulbegovic

  • 1Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. jioannid@stanford.edu

Journal of Clinical Epidemiology
|May 14, 2013
PubMed
Summary

Researchers developed a model to find the best type I and type II error rates for studies with limited sample sizes. This helps maximize study value by balancing correct and incorrect inferences.

More Related Videos

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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: May 11, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Statistical modeling
  • Research methodology
  • Biostatistics

Background:

  • Selecting appropriate statistical error rates is crucial for study validity.
  • Sample size limitations often necessitate trade-offs between different types of errors.
  • Maximizing the informational value of a study under constraints is a key challenge.

Purpose of the Study:

  • To develop a model for optimizing the selection of type I and type II error rates.
  • To maximize the overall value of a study given constraints on sample size.
  • To provide a framework for balancing correct and incorrect inferences.

Main Methods:

  • A composite study value model was developed based on true positives, true negatives, false positives, and false negatives.
  • Multiplicative and additive models were used to quantify study value.
  • The model was applied to diverse research scenarios including randomized trials, epidemiologic studies, and omics investigations with large-scale testing and variable sample sizes.

Main Results:

  • The optimal balance of type I and type II errors is highly dependent on sample size and expected effect sizes.
  • Streamlined equations were derived for specific scenarios, such as equal weighting of all inference types, or situations where true negatives have no value.
  • The model demonstrated how to adjust error rates when a true positive is the minimum requirement for study value.

Conclusions:

  • The derived optimization equations provide a practical tool for researchers.
  • These equations can guide the selection of type I and type II error rates in studies with constrained sample sizes.
  • This approach enhances the value and efficiency of research under practical limitations.