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Related Concept Videos

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...
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...
Coefficient of Variation01:10

Coefficient of Variation

The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
What is Variation?01:14

What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
Variance01:15

Variance

The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.

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Related Experiment Video

Updated: Jun 13, 2026

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

Coordinate dependence of variability analysis.

Dagmar Sternad1, Se-Woong Park, Hermann Müller

  • 1Department of Biology, Northeastern University, Boston, Massachusetts, United States of America. dagmar@neu.edu

Plos Computational Biology
|April 28, 2010
PubMed
Summary
This summary is machine-generated.

Analyzing motor control variability is key to understanding the central nervous system (CNS). This study shows coordinate choices can bias variability analysis, impacting conclusions about neural control strategies.

Related Experiment Videos

Last Updated: Jun 13, 2026

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

Area of Science:

  • Movement neuroscience
  • Motor control
  • Computational neuroscience

Background:

  • Motor performance variability offers insights into central nervous system (CNS) control strategies.
  • Sophisticated neural control is suggested by variability distribution that minimally impacts task performance.
  • Coordinate system choice can significantly influence the analysis of variability, potentially compromising findings.

Purpose of the Study:

  • To assess the influence of coordinate systems on the analysis of motor performance variability.
  • To identify limitations of covariance matrix-based methods due to coordinate dependency.
  • To propose an alternative, less coordinate-sensitive analysis approach.

Main Methods:

  • Critically evaluated covariance matrix analysis methods for motor variability.
  • Identified coordinate sensitivity in anisotropy and orthogonality measures.
  • Developed and proposed a two-level analysis mapping execution variability to performance outcomes.

Main Results:

  • Covariance matrix methods are highly sensitive to coordinate choices, affecting interpretations of neural control.
  • Anisotropy and orthogonality measures are not robust to arbitrary coordinate transformations.
  • The proposed two-level approach demonstrates reduced sensitivity to coordinate systems.

Conclusions:

  • Traditional methods for analyzing motor variability are fundamentally limited by coordinate system selection.
  • Unambiguous inferences about CNS control require coordinate-invariant analysis methods.
  • A two-level analysis offers a promising step towards coordinate-invariant methods in movement neuroscience.