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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...
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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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...
Random and Systematic Errors01:20

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...
Uncertainty in Measurement: Accuracy and Precision03:37

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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.
Propagation of Uncertainty from Systematic Error01:10

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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...

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Sealable Femtoliter Chamber Arrays for Cell-free Biology
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Published on: March 11, 2015

Individual variability is not noise.

Karl Zilles1, Katrin Amunts

  • 1Research Center Jülich, Institute of Neuroscience and Medicine (INM-1), Jülich, Germany. k.zilles@fz-juelich.de

Trends in Cognitive Sciences
|March 20, 2013
PubMed
Summary
This summary is machine-generated.

Intersubject variability in brain functional connectivity differs across the cortex, being highest in multimodal association areas. This variation is crucial for understanding brain development and interpreting neuroimaging results.

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Brain Imaging

Background:

  • Functional connectivity analysis is a key tool in neuroscience.
  • Understanding variations in brain activity between individuals is important.
  • The role of intersubject variability in functional connectivity is not fully understood.

Purpose of the Study:

  • To investigate the heterogeneity of intersubject variability in functional connectivity across the human cortex.
  • To determine if specific cortical regions exhibit higher intersubject variability.
  • To highlight the significance of intersubject variability beyond noise in functional neuroimaging.

Main Methods:

  • Analysis of functional connectivity data from a cohort of individuals.
  • Quantification of intersubject variability across different cortical regions.
  • Comparison of variability levels in unimodal sensory/motor areas versus association cortices.

Main Results:

  • Intersubject variability in functional connectivity is not uniform across the cortex.
  • Multimodal association cortex shows significantly higher intersubject variability compared to other regions.
  • This variability is consistently observed and not attributable to random noise.

Conclusions:

  • Intersubject variability in functional connectivity is a structured biological phenomenon.
  • Higher variability in association cortex may reflect its role in complex cognitive functions and individual differences.
  • Acknowledging and analyzing intersubject variability is essential for advancing our understanding of brain evolution, development, and task-based neuroimaging interpretation.