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

Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Margin of Error01:27

Margin of Error

The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

You might also read

Related Articles

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

Sort by
Same author

A gender-sensitized lifestyle intervention powered by sport fandom in men with overweight or obesity: a 1-year cost-effectiveness analysis of the Hockey FIT Trial.

International journal of obesity (2005)·2026
Same author

The UCEIS and UC-100 score were responsive endoscopic and global indices in a phase 2 trial of ulcerative colitis.

Crohn's & colitis 360·2026
Same author

A rank-based approach to randomized controlled trials with multiple co-primary endpoints of different scales.

Journal of biopharmaceutical statistics·2026
Same author

An Enhanced Treatment Algorithm Targeting Mucosal Healing Is Effective and Safe in Older Patients With Crohn's Disease: A Post Hoc Analysis of the REACT2 Trial.

The American journal of gastroenterology·2026
Same author

Confidence interval estimation for the win probability in cluster randomized trials with hierarchical composite endpoints using win fractions.

Clinical trials (London, England)·2026
Same author

Evaluating treatment to a target of transmural healing in patients with moderately to severely active Crohn's disease: rationale, design and protocol for the randomised controlled VECTORS trial.

BMJ open gastroenterology·2026
Same journal

On the generation and ownership of alpha in medical studies.

Controlled clinical trials·2004
Same journal

An analysis of the effect of funding source in randomized clinical trials of second generation antipsychotics for the treatment of schizophrenia.

Controlled clinical trials·2004
Same journal

Symptom recording in a randomised clinical trial: paper diaries vs. electronic or telephone data capture.

Controlled clinical trials·2004
Same journal

Statistical comparison of random allocation methods in cancer clinical trials.

Controlled clinical trials·2004
Same journal

Analyzing bronchodilation with emphasis on disease type, age and sex.

Controlled clinical trials·2004
Same journal

Geographic variability in patient characteristics, treatment and outcome in an International Trial of Magnesium in acute myocardial infarction.

Controlled clinical trials·2004
See all related articles

Related Experiment Video

Updated: May 8, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

A simple alternative confidence interval for the difference between two proportions.

Guangyong Zou1, Allan Donner

  • 1Robarts Clinical Trials, Robarts Research Institute, London, Ontario, Canada N6A 5K8. gzou@robarts.ca

Controlled Clinical Trials
|February 26, 2004
PubMed
Summary
This summary is machine-generated.

A new method using Fisher's z transformation provides reliable confidence intervals for the difference between two proportions. This simple approach performs comparably to existing methods in comparative studies with binary outcomes.

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
06:48

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil

Published on: July 29, 2020

Related Experiment Videos

Last Updated: May 8, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
06:48

Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil

Published on: July 29, 2020

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Inference

Background:

  • Comparing two proportions is crucial in clinical trials with binary outcomes.
  • Interval estimation for this difference is a key area of statistical research.
  • Newcombe's hybrid procedure is a recommended method due to its performance and simplicity.

Purpose of the Study:

  • To propose a novel, simple alternative for interval estimation of the difference between two proportions.
  • To evaluate the performance of the proposed method against established procedures.

Main Methods:

  • A new procedure based on Fisher's z transformation was developed.
  • An exact evaluation study was conducted to assess performance metrics.
  • Comparison with Newcombe's hybrid procedure was performed.

Main Results:

  • The proposed Fisher's z transformation method demonstrates comparable performance to Newcombe's procedure.
  • Key performance indicators include percent coverage and expected confidence interval width.
  • The new method is shown to be a viable alternative for statistical analysis.

Conclusions:

  • Fisher's z transformation offers a simple and effective approach for confidence interval estimation of the difference between two proportions.
  • This method is suitable for prospective comparative studies, including randomized controlled trials.
  • The findings support the adoption of this new procedure in biostatistical practice.