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

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.4K
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...
10.4K
Confidence Intervals01:21

Confidence Intervals

11.2K
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...
11.2K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.1K
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...
9.1K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

12.1K
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...
12.1K
Confidence Coefficient01:24

Confidence Coefficient

10.9K
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...
10.9K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.7K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Assessing myxozoan diversity in fish: microscopy-based detection versus high-throughput amplicon sequencing.

International journal for parasitology·2026
Same author

Mamba2SVN: a Mamba2 and reconstruction-cooperative sensitivity refinement-based variational network for parallel MRI reconstruction.

Physics in medicine and biology·2026
Same author

Anion-Diluent Decoupled Solvation Chemistry in Ionic Liquid-Based Localized High-Concentration Electrolytes Toward High-Voltage Lithium Metal Batteries.

Nano-micro letters·2026
Same author

CMPK2 promotes M1 macrophage polarization in sepsis-induced acute lung injury via NLRP3/NF-κB signalling.

International immunopharmacology·2026
Same author

Osteocytic Pink1/Prkn modulates M6P metabolism and contributes to GC-induced bone loss.

Journal of orthopaedic translation·2026
Same author

Natural Products as NLRP3 Inflammasome Inhibitors: A Review.

Molecules (Basel, Switzerland)·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 26, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.5K

Exact confidence intervals for the average causal effect on a binary outcome.

Xinran Li1, Peng Ding2

  • 1Department of Statistics, Harvard University, Cambrdige, 02138, MA, U.S.A.

Statistics in Medicine
|February 3, 2016
PubMed
Summary
This summary is machine-generated.

Researchers developed more efficient methods for exact confidence intervals in causal effect studies. These new approaches improve computational speed for analyzing binary outcomes in randomized experiments.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K

Related Experiment Videos

Last Updated: Mar 26, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K

Area of Science:

  • Statistics
  • Biostatistics
  • Causal Inference

Background:

  • Rigdon and Hudgens proposed two methods for exact confidence intervals for average causal effects in binary outcomes.
  • Their second method involves O(n4) randomization tests, posing computational challenges.

Purpose of the Study:

  • To develop more computationally efficient methods for constructing exact confidence intervals.
  • To leverage physical randomization principles for improved statistical analysis.

Main Methods:

  • Exploiting advances in hypergeometric confidence intervals.
  • Utilizing stochastic order information from randomization tests.
  • Proposing methods that avoid Monte Carlo or require at most O(n2) randomization tests.

Main Results:

  • Demonstrated alternative ways to construct exact confidence intervals based on physical randomization.
  • Developed computationally efficient approaches compared to existing methods.
  • Provided R code for practical implementation.

Conclusions:

  • The proposed methods offer significant computational advantages for exact confidence intervals.
  • These new approaches enhance the feasibility of exact inference in causal effect studies.
  • The study provides practical tools for researchers in biostatistics and related fields.