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

9.3K
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 confidence...
9.3K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

9.9K
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...
9.9K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.8K
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...
8.8K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.6K
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...
7.6K
Critical Values01:31

Critical Values

9.5K
A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
9.5K
Confidence Coefficient01:24

Confidence Coefficient

9.2K
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...
9.2K

You might also read

Related Articles

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

Sort by
Same author

Inhalable viromimetic polymer nanoparticle vaccine (iVPNVax) in a subcutaneous-prime/inhalation-boost vaccination schedule for eliciting durable mucosal and systemic immune protection.

Biomaterials science·2026
Same author

Polygonatum sibiricum Alleviates Salivary Gland Dysfunction in Sjögren's Syndrome via CHRM3/AQP5 Axis Potentiation.

Planta medica·2026
Same author

Hydrogen Bond Reconstruction Maneuver in Eutectic Electrolyte Enables Ultralong-Lifespan Zinc-Ion Batteries.

Journal of the American Chemical Society·2025
Same author

The Combined Effects of Pesticide Behavior and Target Specificity Determine the Diversity of Pesticide Toxicity to Bees.

Environmental science & technology·2025
Same author

FEMC-deuterogenic artificial solid electrolyte interphase boosts high-performance sodium-ion batteries.

Chemical communications (Cambridge, England)·2025
Same author

The time-averaged serum uric acid can better predict the prognosis of IgA nephropathy.

Nutrition, metabolism, and cardiovascular diseases : NMCD·2024
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Apr 29, 2026

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

2.3K

Simultaneous confidence bands for low-dose risk estimation with quantal data.

Jianan Peng1, Megan Robichaud, Abdelaziz Q Alsubie

  • 1Department of Mathematics and Statistics, Acadia University, Wolfville, NS, B4P 2R6, Canada.

Biometrical Journal. Biometrische Zeitschrift
|May 20, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for estimating safe low dose levels of toxic agents using high-dose data. The approach enhances risk assessment accuracy for human, animal, and ecological safety evaluations.

Keywords:
Benchmark analysisMultistage modelSimultaneous hyperbolic bandSimultaneous three-segment band

More Related Videos

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

19.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.9K

Related Experiment Videos

Last Updated: Apr 29, 2026

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

2.3K
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

19.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.9K

Area of Science:

  • Toxicology and Risk Assessment
  • Biostatistics
  • Environmental Health

Background:

  • Risk assessment for toxic agents is challenging due to limited low-dose data.
  • Existing methods like hyperbolic confidence bands have limitations for precise low-dose estimation.
  • Quantal data from high-dose studies are often the only available information.

Purpose of the Study:

  • To propose a novel statistical method for accurate low-dose risk estimation.
  • To develop simultaneous upper confidence limits for extra risks.
  • To establish simultaneous lower bounds for benchmark dose determination.

Main Methods:

  • Development of a new three-segment confidence band approach.
  • Application to quantal toxicological data.
  • Validation through real-world data analysis and simulation studies.

Main Results:

  • The proposed three-segment confidence bands provide reliable upper confidence limits on extra risks.
  • Simultaneous lower bounds on the benchmark dose were effectively constructed.
  • The method demonstrated improved precision in low-dose risk assessment compared to existing techniques.

Conclusions:

  • The novel three-segment confidence band method offers a robust tool for low-dose risk assessment.
  • This approach enhances the safety evaluation of toxic agents using high-dose quantal data.
  • The findings have significant implications for regulatory toxicology and public health protection.