Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Data Validation

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

Development of Analytical Methods

Development of Analytical Methods

An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...

Bioequivalence Data: Statistical Interpretation

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...

You might also read

Related Articles

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

Sort by

Same author

Factors affecting serum PFAS concentrations among US females with surgically and naturally induced menopause: data from NHANES 2003-2018.

Environmental science and pollution research international·2023

Same author

Co-variate adjusted associations between serum concentrations of selected perfluoroalkyl substances and urinary concentrations of selected arsenic species.

Environmental science and pollution research international·2022

Same author

Associations between concentrations of serum α-klotho and selected urinary monohydroxy metabolites of polycyclic aromatic hydrocarbons: data for US adults aged 40-79 years.

Environmental science and pollution research international·2022

Same author

Serum concentrations of perfluoro-1-heptane sulfonate (PFHpS) among US adults: variabilities across different stages of kidney function.

Environmental science and pollution research international·2022

Same author

Associations of serum perfluoroalkyl substances with concentrations of blood manganese and selenium.

Environmental science and pollution research international·2022

Same author

Serum concentrations of selected perfluoroalkyl substances for US females compared to males as they age.

The Science of the total environment·2022

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis

Published on: March 17, 2010

A simple methodology to analyze inter-laboratory data: a simulation study.

¹Division of Laboratory Statistics, National Center for Environmental Health, Centers for Disease Control and Prevention, Mail Stop F-47, Atlanta, GA 30341, United States. Rij0@cdc.gov

Clinica Chimica Acta; International Journal of Clinical Chemistry

|October 6, 2009

Summary

A new 3-step methodology effectively analyzes inter-laboratory experiments, identifying labs with high variance or means. An optimal sample size of 20 ensures accurate assessment of measurement reproducibility and accuracy.

More Related Videos

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Published on: May 20, 2013

Related Experiment Videos

Last Updated: Jun 19, 2026

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis

The Importance of Correct Protein Concentration for Kinetics and Affinity Determination in Structure-function Analysis

Published on: March 17, 2010

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Published on: May 20, 2013

Area of Science:

Analytical Chemistry
Metrology

Background:

Inter-laboratory experiments assess measurement accuracy and reproducibility across different labs, methods, and equipment.
Variability in procedures can impact chemical concentration measurements.
A novel 3-step methodology is proposed for analyzing these experiments.

Purpose of the Study:

To introduce and validate a 3-step methodology for analyzing inter-laboratory experiments.
To assess the methodology's effectiveness in identifying laboratories with outlying performance metrics.
To determine the optimal sample size for reliable analysis.

Main Methods:

A simulation study involving 500 iterations with 12 laboratories was performed.
Analysis of variance (ANOVA) and recursive algorithms were used to identify and remove laboratories with high variance or means.
Consensus statistics including mean, standard deviation (SD), repeatability, and reproducibility were computed.

Main Results:

Laboratories with excessive variance were consistently removed when sample size was >=20.
Laboratories with high means were almost always identified, regardless of sample size.
Observed bias and imprecision remained within acceptable limits (+/-3.2% and +/-10.4%, respectively).

Conclusions:

The proposed 3-step methodology reliably identifies laboratories with outlying variances or means.
An optimal sample size of 20 is recommended for effective laboratory performance assessment.
The methodology enhances the accuracy and reproducibility assessment in inter-laboratory studies.