Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Types of Hypothesis Testing

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.

Accuracy and Errors in Hypothesis Testing

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...

Null and Alternative Hypotheses

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...

Decision Making: Traditional Method

Decision Making: Traditional Method

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...

What is a Hypothesis?

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague statement. It...

You might also read

Related Articles

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

Sort by

Same author

Fascial plane blocks in pediatric patients.

Journal of anesthesia, analgesia and critical care·2026

Same author

Does timing matter? Comparing pre- and postoperative erector spinae plane blocks in lumbar spine surgery.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026

Same author

Clinical Decision Support to Reduce Opioid Prescribing at Discharge for Inpatients Undergoing Surgery (LESS Study): An Interrupted Time Series Analysis.

Anesthesiology·2026

Same author

Intervals For Oscillometric Arterial Pressure Monitoring During Noncardiac Surgery: the '2.5-Minute Versus 5-Minute' Randomized Clinical Trial.

Anesthesiology·2026

Same author

Responsible use of large language models in manuscript authorship, peer review, and editorial processes: a Delphi consensus among editors-in-chief of anaesthesia and pain medicine journals (RULE-AP).

British journal of anaesthesia·2026

Same author

Perioperative Hypotension and Delirium: Reply.

Anesthesiology·2026

Same journal

Incomplete Recovery of Diaphragmatic Mechanics at a Train-of-Four Ratio of 0.90 to <0.95 Assessed by Dynamic Digital Radiography: A Proof-of-Concept Case Series.

Anesthesia and analgesia·2026

Same journal

Patients' Perspectives and Experiences of Participating in Anesthesia Research: A Qualitative Study.

Anesthesia and analgesia·2026

Same journal

Impact of Epidural-Related Maternal Fever on Neonatal Outcomes: A Single-Center Retrospective Case-Control Study Excluding Confirmed Histological Chorioamnionitis.

Anesthesia and analgesia·2026

Same journal

Patient Beliefs and Experiences of Adhering to Medical Therapies for Cardiovascular Comorbidities, Before Noncardiac Elective Surgery in South Africa: A Mixed-Methods Study.

Anesthesia and analgesia·2026

Same journal

Beyond Administrative Indices: Allostatic Load and Autonomic Dyshomeostasis as Biological Mediators of Intraoperative Cardiac Arrest.

Anesthesia and analgesia·2026

Same journal

In Response.

Anesthesia and analgesia·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 22, 2026

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

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

Joint hypothesis testing and gatekeeping procedures for studies with multiple endpoints.

Edward J Mascha¹, Alparslan Turan

¹Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, USA. maschae@ccf.org

Anesthesia and Analgesia

|May 5, 2012

Summary

This summary is machine-generated.

Joint hypothesis testing and gatekeeping procedures improve multi-outcome study interpretation. These methods protect statistical integrity when evaluating interventions across several endpoints, enhancing clinical trial efficiency.

More Related Videos

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

Published on: April 8, 2015

Related Experiment Videos

Last Updated: May 22, 2026

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

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

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

The Modified Hole Board - Measuring Behavior, Cognition and Social Interaction in Mice and Rats

Published on: April 8, 2015

Area of Science:

Biostatistics
Clinical Trials
Pharmacology

Background:

Evaluating interventions with multiple outcomes is complex.
Analyzing outcomes individually can lead to conflicting results and misinterpretation.
A statistical framework is needed to manage multiple endpoints effectively.

Purpose of the Study:

To advocate for joint hypothesis testing and gatekeeping procedures in multi-outcome studies.
To demonstrate methods for claiming intervention superiority on at least one outcome while ensuring noninferiority on others.
To improve the interpretation and efficiency of clinical trials with multiple endpoints.

Main Methods:

Joint hypothesis testing with pre-specified decision rules to control Type I error.
Gatekeeping procedures (serial and parallel) for ordered hypothesis testing.
Application of methods to a randomized controlled trial on transdermal nicotine for post-operative pain and opioid use.

Main Results:

Joint hypothesis testing and gatekeeping procedures protect the overall Type I error rate.
These methods enhance the efficiency and interpretability of multi-outcome studies.
Demonstrated application in a trial assessing transdermal nicotine's effect on pain and opioid consumption.

Conclusions:

Joint hypothesis testing and gatekeeping procedures offer a robust statistical approach for multi-outcome evaluations.
These methods provide clearer decision-making frameworks in clinical research.
Improved statistical strategies lead to more reliable conclusions in complex intervention studies.