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Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
Variation01:19

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Mean Absolute Deviation01:13

Mean Absolute Deviation

The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
Calculating Standard Deviation01:08

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high variation.       
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Related Experiment Video

Updated: May 16, 2026

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
11:38

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

Published on: October 4, 2024

A novel "Integrated Biomarker Response" calculation based on reference deviation concept.

Wilfried Sanchez1, Thierry Burgeot, Jean-Marc Porcher

  • 1Institut National de l'Environnement Industriel et des Risques, unité d'écotoxicologie in vitro et in vivo, 60550 Verneuil en Halatte, France. wilfried.sanchez@ineris.fr

Environmental Science and Pollution Research International
|December 5, 2012
PubMed
Summary

Environmental managers can now better assess ecosystem health using the novel Integrated Biological Responses version 2 (IBRv2) index. This tool simplifies large multi-biomarker datasets, improving regulatory application and environmental stress impact analysis.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Last Updated: May 16, 2026

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Environmental toxicology
  • Ecotoxicology
  • Biomonitoring

Background:

  • Multi-biomarker approaches are crucial for assessing ecosystem health and detecting environmental stress impacts on organisms.
  • Analyzing large biomarker datasets poses a significant challenge for environmental managers in regulatory contexts.
  • Existing integrative tools for summarizing biomarker responses have limitations.

Purpose of the Study:

  • To update the calculation of the Integrated Biological Response (IBR) index to overcome its previously identified weaknesses.
  • To introduce a novel index, Integrated Biological Responses version 2 (IBRv2), based on the reference deviation concept.
  • To provide an improved tool for integrating multi-biomarker responses in environmental monitoring.

Main Methods:

  • The study presents a novel index, IBRv2, building upon the reference deviation concept.
  • The calculation method is an update to the original IBR index described by Beliaeff and Burgeot (2002).
  • The new index was evaluated for its ability to discriminate sampling sites and interpret biomarker responses.

Main Results:

  • The IBRv2 index allows for clear discrimination between sampling sites, similar to the original IBR.
  • Significant differences in site classification were observed for contaminated sites when considering both up- and downregulation of biomarker responses.
  • The IBRv2 demonstrates enhanced ability to differentiate sites based on complex biomarker patterns.

Conclusions:

  • The IBRv2 is a valuable new tool for integrating multi-biomarker responses in environmental assessments.
  • This updated index addresses limitations of previous integrative tools, enhancing regulatory applicability.
  • IBRv2 can be effectively applied to large-scale monitoring programs and upstream/downstream investigations.