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

6.3K
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...
6.3K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.3K
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...
7.3K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.8K
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...
5.8K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
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...
4.1K
Confidence Coefficient01:24

Confidence Coefficient

7.6K
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...
7.6K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K

You might also read

Related Articles

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

Sort by
Same author

Use of the Workbook Method to estimate the prevalence of chronic hepatitis B infections in the European Union and European Economic Area, 2022.

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2026
Same author

Securing Commercial Nucleic Acid Synthesis.

Rand health quarterly·2024
Same author

A protocol for identifying the needs related to drug use, health and social (re)integration of people living in prison within five European countries.

Archives of public health = Archives belges de sante publique·2024
Same author

Prevalence of chronic HCV infection in EU/EEA countries in 2019 using multiparameter evidence synthesis.

The Lancet regional health. Europe·2024
Same author

Prevalence of drug use before and during imprisonment in seven European countries (2014-2018).

Journal of community psychology·2023
Same author

Geographic and temporal trends in fentanyl-detected deaths in Connecticut, 2009-2019.

Annals of epidemiology·2023
Same journal

Direct-Assisted Bayesian Unit-level Modeling for Small Area Estimation of Rare Event Prevalence.

Journal of survey statistics and methodology·2026
Same journal

Toward a Principled Workflow for Prevalence Mapping Using Household Survey Data.

Journal of survey statistics and methodology·2026
Same journal

MEETING DATA COLLECTION GOALS QUICKER: AN EXPERIMENTAL EVALUATION TO REDUCE FIELDWORK DURATION IN A MIXED-MODE PANEL STUDY.

Journal of survey statistics and methodology·2026
Same journal

COMPARATIVE EFFECTIVENESS OF PROPENSITY SCORE ESTIMATION METHODS FOR INVERSE PROBABILITY OF TREATMENT WEIGHTING ANALYSIS WITH COMPLEX SURVEY DATA: A SIMULATION STUDY.

Journal of survey statistics and methodology·2025
Same journal

Synthesizing Surveys with Multiple Units of Observation: An Application to the Longitudinal Aging Study in India.

Journal of survey statistics and methodology·2025
Same journal

Analyzing Potential Non-Ignorable Selection Bias in an Off-Wave Mail Survey Implemented in a Long-Standing Panel Study.

Journal of survey statistics and methodology·2025
See all related articles

Related Experiment Video

Updated: Jul 11, 2025

Quantifying Corticolous Arthropods Using Sticky Traps
05:28

Quantifying Corticolous Arthropods Using Sticky Traps

Published on: January 19, 2020

5.5K

Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.

Jinghao Sun1, Luk Van Baelen2, Els Plettinckx3

  • 1is a PhD Candidate in Biostatistics at the Yale School of Public Health, New Haven, CT, USA.

Journal of Survey Statistics and Methodology
|November 17, 2023
PubMed
Summary
This summary is machine-generated.

Capture-recapture surveys can now estimate population size with partial identification, offering a credible confidence set even with convenience samples. This method provides more reliable estimates for hard-to-count populations.

Keywords:
BootstrapInjection drug usePartial identificationPopulation sizeProfile likelihood

More Related Videos

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

9.1K
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

2.1K

Related Experiment Videos

Last Updated: Jul 11, 2025

Quantifying Corticolous Arthropods Using Sticky Traps
05:28

Quantifying Corticolous Arthropods Using Sticky Traps

Published on: January 19, 2020

5.5K
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

9.1K
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

2.1K

Area of Science:

  • Statistics
  • Epidemiology
  • Social Sciences

Background:

  • Capture-recapture (CRC) surveys estimate populations not directly enumerated.
  • Existing methods often require stringent assumptions for point identification, limiting real-world applicability.
  • Convenience samples in CRC surveys can compromise the empirical credibility of population size estimates.

Purpose of the Study:

  • To apply partial identification theory to CRC surveys.
  • To develop methods for constructing confidence sets for population size under weak assumptions.
  • To improve the empirical credibility of population estimates from heterogeneous survey data.

Main Methods:

  • Utilized partial identification theory to address unobserved data in contingency tables.
  • Developed confidence sets using bounds on pairwise capture probabilities.
  • Employed test inversion bootstrap and profile likelihood confidence intervals.

Main Results:

  • Demonstrated that weak assumptions or qualitative knowledge yield nontrivial confidence sets for population size.
  • Simulation results showed well-calibrated confidence sets for both proposed methods.
  • Successfully applied the methodology to estimate the population of people who inject drugs in Brussels.

Conclusions:

  • Partial identification offers a robust framework for CRC surveys with convenience samples.
  • The developed methods provide empirically credible confidence sets for population size estimation.
  • This approach enhances the reliability of estimates for sensitive or hidden populations.