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

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

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...

Voltammetry: Factors Affecting Measurements

Voltammetry: Factors Affecting Measurements

A current produced due to the redox reactions of the analyte at the working and auxiliary electrodes is called a faradaic current. The reaction can be divided into two types. The current generated due to the reduction of the analyte is called cathodic current, and it carries a positive charge. In contrast, the current produced by analyte oxidation is known as an anodic current, and it has a negative charge. The applied potential at the working electrode determines the faradaic current flow, and...

Random and Systematic Errors

Random and Systematic Errors

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...

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:

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

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.

You might also read

Related Articles

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

Sort by

Same author

Collateral sensitivity and cross-resistance in six species of bacteria exposed to six classes of antibiotics.

Microbiology spectrum·2025

Same author

<i>De novo</i> acquisition of antibiotic resistance in six species of bacteria.

Microbiology spectrum·2025

Same author

Role of RelA-synthesized (p)ppGpp and ROS-induced mutagenesis in <i>de novo</i> acquisition of antibiotic resistance in <i>E. coli</i>.

iScience·2024

Same author

Shifts in phytoplankton and zooplankton communities in three cyanobacteria-dominated lakes after treatment with hydrogen peroxide.

Harmful algae·2024

Same author

The Effect of the Stringent Response and Oxidative Stress Response on Fitness Costs of De Novo Acquisition of Antibiotic Resistance.

International journal of molecular sciences·2024

Same author

Dynamics of <i>Thioalkalivibrio</i> species in a co-culture under selective pressure of ampicillin.

FEMS microbes·2023

Search research articles

Related Experiment Video

Updated: Jun 21, 2026

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Published on: August 6, 2013

Variations in MIC value caused by differences in experimental protocol.

J Merijn Schuurmans¹, Anmar S Nuri Hayali, Belinda B Koenders

¹Laboratory for Molecular Biology and Microbial Food Safety, Swammerdam Institute of Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands.

Journal of Microbiological Methods

|July 29, 2009

Summary

Different methods for measuring antibiotic effectiveness (minimal inhibitory concentration or MIC) can yield results varying by up to eightfold. This impacts comparisons of absolute MIC values between studies, though trends within a single protocol remain consistent.

More Related Videos

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

Published on: October 4, 2013

Related Experiment Videos

Last Updated: Jun 21, 2026

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Published on: August 6, 2013

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

Published on: October 4, 2013

Area of Science:

Microbiology
Pharmacology
Antimicrobial Resistance

Background:

The minimal inhibitory concentration (MIC) is a key metric for antibiotic efficacy.
Standardization of MIC measurement protocols is crucial for comparable results.
Variations in experimental conditions can significantly influence MIC values.

Purpose of the Study:

To investigate how variations in MIC measurement protocols affect antibiotic efficacy estimates.
To quantify the impact of different factors on MIC values for specific microorganism-antibiotic combinations.

Main Methods:

Examined variations in MIC estimates for E. coli with amoxicillin and tetracycline.
Investigated MIC variations for Pseudomonas putida with enrofloxacin.
Assessed the influence of measurement duration, initial culture density, growth determination method (optical density vs. cell counts), and resistance induction.

Main Results:

Factors such as measurement duration, starting culture density, and growth determination method can alter MIC values by up to a factor of 8.
Induction of resistance also contributes to variability in MIC measurements.
Observed significant differences in MIC estimates for E. coli and P. putida across tested conditions.

Conclusions:

Protocol variations introduce substantial variability into MIC measurements, potentially affecting absolute efficacy assessments.
While trends may be consistent within a single protocol, direct comparison of absolute MIC values across different studies requires caution.
Further standardization efforts are needed to ensure reliable and comparable MIC data in antimicrobial research.