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

Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.1K
Contaminants and Errors01:16

Contaminants and Errors

437
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
437
Bias01:22

Bias

7.7K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.7K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.5K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.5K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

794
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
794
Stratified Sampling Method01:16

Stratified Sampling Method

15.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
15.8K

You might also read

Related Articles

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

Sort by
Same author

Spatiotemporal characterization of single-stranded DNA intermediates after UV irradiation: II. Rapid growth and effects of recA and recJ.

PLoS genetics·2026
Same author

Spatiotemporal characterization of single-stranded DNA Intermediates after UV Irradiation: I: Post-replication gaps formed during slow growth.

PLoS genetics·2026
Same author

Gonococcal Infective Endocarditis: The Importance of a Sexual History.

Cureus·2026
Same author

Predicting resilience among health social workers during COVID-19.

Journal of health psychology·2026
Same author

The enigmatic Isthmian inscriptions<b>The Isthmian Script</b> <i>Martha J. Macri</i> University of Oklahoma Press, 2026. 168 pp.

Science (New York, N.Y.)·2026
Same author

Can Patients Self-Identify Gait Disturbances After Lower Extremity Trauma? Enhancing Patient Engagement in Their Care.

Journal of clinical medicine·2026
Same journal

Jack Fowle: Combining Values, Experience, and Teamwork to Improve Risk Analysis.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

A Hybrid FMEA-AHP Framework for Risk Prioritization in Nontransparent Artificial Intelligence Systems.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Trust-Building Communication for Extreme Heat Preparedness.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Spring Broken: A Risk Analysis of Fatal and Nonfatal Traffic Injuries in Florida.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Global Sensitivity Analysis of Societal Resilience Using Shapley Values and Polynomial Chaos Expansion.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Assessing How Fact-Checks Influence Accuracy and Consensus Judgments: Evidence From the Olympics.

Risk analysis : an official publication of the Society for Risk Analysis·2026
See all related articles

Related Experiment Video

Updated: Mar 1, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Bias-Corrected Estimation in Continuous Sampling Plans.

Geoffrey Decrouez1, Andrew Robinson2

  • 1National Research University, Higher School of Economics, Moscow, Russia.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|May 31, 2017
PubMed
Summary
This summary is machine-generated.

This study addresses bias in estimating failure rates from continuous sampling plans (CSPs). We developed bias-corrected estimators for improved quality control and biosecurity monitoring.

Keywords:
Border inspectioncontinuous sampling planspoint and interval estimation

More Related Videos

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.8K
Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
08:53

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

1.0K

Related Experiment Videos

Last Updated: Mar 1, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.8K
Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
08:53

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

1.0K

Area of Science:

  • Industrial Engineering
  • Statistical Quality Control
  • Biosecurity Monitoring

Background:

  • Continuous Sampling Plans (CSPs) are vital for production line quality control.
  • Existing CSPs lack robust statistical estimators for failure rate data.
  • Accurate failure rate estimation impacts process management and policy decisions.

Purpose of the Study:

  • To develop statistically sound estimators for failure rates derived from CSP inspection processes.
  • To address the bias in maximum likelihood estimation of failure rates under CSPs.
  • To support biosecurity compliance monitoring for imported goods.

Main Methods:

  • Analysis of bias in maximum likelihood estimation under various CSPs.
  • Derivation of explicit expressions for bias contributions.
  • Construction of bias-corrected estimators and confidence intervals.
  • Numerical evaluation of estimator performance.

Main Results:

  • Maximum likelihood estimation of failure rates under CSPs can be biased.
  • Explicit bias expressions were derived for different CSPs.
  • Bias-corrected estimators and confidence intervals were developed.

Conclusions:

  • The developed estimators offer improved accuracy for failure rate assessment in CSPs.
  • This research enhances quality control and informs policy for processes using CSPs.
  • Findings are applicable to biosecurity monitoring and other quality assurance applications.