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 Experiment Video

Updated: Jan 17, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.4K

Using Aggregated Proficiency Testing Results to Identify Systematic Error.

Uzay Kırbıyık1, J Rex Astles1

  • 1Division of Laboratory Systems, OLSR (Office of Laboratory Systems and Response), Centers for Disease Control and Prevention (CDC), Atlanta, GA, United States.

The Journal of Applied Laboratory Medicine
|September 19, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

9.3K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
9.3K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.8K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.8K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

5.0K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
5.0K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

562
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
562
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

99.9K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
99.9K
Random and Systematic Errors01:20

Random and Systematic Errors

14.4K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.4K

You might also read

Related Articles

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

Sort by
Same author

Network Characteristics and Visualization of COVID-19 Outbreak in a Large Detention Facility in the United States - Cook County, Illinois, 2020.

MMWR. Morbidity and mortality weekly report·2020
Same journal

Benchmarking Institutional Support for Point-of-Care Testing Programs: Scale, Staffing, and Operational Challenges.

The journal of applied laboratory medicine·2026
Same journal

Hidden Figures of DNA. 1. June (Broomhead) Lindsey.

The journal of applied laboratory medicine·2026
Same journal

The Emerging Role of Blood-Based Biomarkers in Predicting Alzheimer's Disease.

The journal of applied laboratory medicine·2026
Same journal

Healthcare Excellence in 2026: An Unprecedented Sweep for Global Growth Economies.

The journal of applied laboratory medicine·2026
Same journal

ADLM Guidance Document on Incorporating Gender Diversity in Pathology and Laboratory Medicine.

The journal of applied laboratory medicine·2026
Same journal

Leveraging an Explainable Machine Learning Model for Early Identification of Acute Kidney Injury: A Retrospective Study.

The journal of applied laboratory medicine·2026
See all related articles

Proficiency testing (PT) effectively detects systematic errors in clinical laboratories. However, the magnitude of detected systematic error varies with different acceptance limit types, with 3 standard deviations (3SD) showing a lesser effect.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Quality Assurance

Background:

  • Proficiency testing (PT) is crucial for identifying recurring systematic errors in laboratory testing.
  • Clinical Laboratory Improvement Amendments (CLIA) utilize acceptance limits (ALs), including 3 standard deviations (3SD) and concentration limits, to evaluate PT performance.

Purpose of the Study:

  • To investigate the capability of PT to detect systematic error.
  • To assess how different types of CLIA ALs influence the detection of systematic error.

Main Methods:

  • Analyzed CLIA laboratory PT data from 2008-2018, excluding irregular scores.
  • Calculated miss rates and unsatisfactory event rates (<80 score).
  • Compared observed event scores to expected scores using statistical tests and binomial distribution to quantify systematic effects.

More Related Videos

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.3K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K

Related Experiment Videos

Last Updated: Jan 17, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.4K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.3K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K

Main Results:

  • A total of 151,401,128 event scores from 40,596 laboratories for 75 analytes were analyzed.
  • The distribution of event scores was skewed towards misses, indicating more errors than predicted by random chance.
  • Miss and unsatisfactory rates were higher in short-term PT participants, and systematic error was substantial, though less pronounced with 3SD ALs.

Conclusions:

  • PT misses are often dependent events, not purely random.
  • All evaluated ALs successfully detected systematic error.
  • Quantifying systematic error from PT data can aid in identifying and correcting analytical issues in laboratories.