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...
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...
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...
Prediction Intervals01:03

Prediction Intervals

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. 
The...
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...

You might also read

Related Articles

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

Sort by
Same author

Does insulin bolster antioxidant defenses via the extracellular signal-regulated kinases-protein kinase B-nuclear factor erythroid 2 p45-related factor 2 pathway?

Antioxidants & redox signaling·2011
Same author

Decrease in calcium-sensing receptor in the progress of diabetic cardiomyopathy.

Diabetes research and clinical practice·2011
Same author

JAMIE: A software tool for jointly analyzing multiple ChIP-chip experiments.

Methods in molecular biology (Clifton, N.J.)·2011
Same author

Morphine-induced conditioned place preference in mice: metabolomic profiling of brain tissue to find "molecular switch" of drug abuse by gas chromatography/mass spectrometry.

Analytica chimica acta·2011
Same author

[The interventions effect-assessment of the workers exposed to N, N-dimethylformamide by percutaneous in a synthetic leather factory].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2011
Same author

[The analysis of effect of Th1/Th2 cytokine in the different prognosis in severe influenza A (H1N1)].

Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology·2011
Same journal

Direct and Indirect Genetic Effects on Child ADHD Traits in Early and Mid-Childhood: Trio Genome-Wide Complex Trait Analyses in a Large Norwegian Birth Registry Cohort.

Behavior genetics·2026
Same journal

Behavioral Disinhibition Model of Addiction: A Review and New Findings from the Minnesota Twin Family Study.

Behavior genetics·2026
Same journal

Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness.

Behavior genetics·2026
Same journal

Genetic and Environmental Associations Between Processing Speed and Executive Functions Across Adolescence and Established Adulthood.

Behavior genetics·2026
Same journal

Heritability of Functional Literacy: Evidence from a Classical Twin Design.

Behavior genetics·2026
Same journal

Geneticists' Understandings of Genetic Causation.

Behavior genetics·2026
See all related articles

Related Experiment Video

Updated: May 18, 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

Adjusted confidence intervals for a bounded parameter.

Hao Wu1, Michael C Neale

  • 1Department of Psychology, Boston College, 300 McGuinn, 140 Commonwealth Avenue, Chestnut Hil, MA 02467, USA. hao.wu.5@bc.edu

Behavior Genetics
|September 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces adjusted confidence intervals for parameters with lower bounds, like variance components. These adjustments ensure consistent conclusions between hypothesis tests and interval estimates, improving statistical validity.

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

Related Experiment Videos

Last Updated: May 18, 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

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

Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Genetics

Background:

  • Standard likelihood ratio tests are invalid for parameters at a boundary, such as zero variance.
  • Existing adjusted tests for variance components do not address confidence interval adjustments.
  • Unadjusted confidence intervals can conflict with hypothesis test results, causing interpretation issues.

Purpose of the Study:

  • To propose adjusted confidence interval methods for parameters with a lower bound.
  • To ensure compatibility between adjusted hypothesis tests and interval estimates.
  • To address conflicting conclusions arising from unadjusted confidence intervals.

Main Methods:

  • Developed two novel methods for adjusting confidence intervals: one Wald-based and one likelihood ratio-based.
  • Ensured proposed methods are compatible with existing adjusted hypothesis tests.
  • Demonstrated parametrization-invariance for the proposed interval adjustment techniques.

Main Results:

  • The proposed adjusted confidence intervals resolve conflicts between hypothesis tests and interval estimates for lower-bounded parameters.
  • Both Wald-based and likelihood ratio-based adjustments provide consistent statistical inference.
  • Simulation studies and real-world examples in ACDE twin models validate the methods.

Conclusions:

  • Adjusted confidence intervals are crucial for accurate statistical inference when testing parameters at a boundary.
  • The proposed methods offer reliable and consistent statistical conclusions in variance component analysis.
  • These advancements are particularly relevant for complex models like those in twin studies.