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Related Concept Videos

Standard Deviation01:10

Standard Deviation

27.6K
The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Mean Absolute Deviation01:13

Mean Absolute Deviation

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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...
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

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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...
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Calculating Standard Deviation01:08

Calculating Standard Deviation

12.5K
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...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Related Experiment Video

Updated: Jan 22, 2026

Ex Vivo Preparations of the Intact Vomeronasal Organ and Accessory Olfactory Bulb
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Ex Vivo Preparations of the Intact Vomeronasal Organ and Accessory Olfactory Bulb

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Olfactory Bulb Microstructural Changes in Patients With Nasal Septum Deviation.

Kerim Aslan1, Dilek Saglam2, Hediye Pinar Gunbey3

  • 1Department of Radiology, Ondokuz Mayis University Faculty of Medicine, Samsun.

The Journal of Craniofacial Surgery
|July 14, 2019
PubMed
Summary

Nasal septum deviation (NSD) is linked to olfactory bulb (OB) microstructural changes, including decreased fractional anisotropy (FA) and increased apparent diffusion coefficient (ADC). These changes correlate with NSD severity and indicate axonal damage.

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Quadruple Immunostaining of the Olfactory Bulb for Visualization of Olfactory Sensory Axon Molecular Identity Codes
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Area of Science:

  • Neuroimaging
  • Otolaryngology
  • Medical Physics

Background:

  • Nasal septum deviation (NSD) can affect sinonasal function and potentially impact adjacent neural structures.
  • The olfactory bulb (OB), responsible for processing smell, may be susceptible to microstructural alterations due to NSD.

Purpose of the Study:

  • To evaluate microstructural changes in the olfactory bulb (OB) of patients with nasal septum deviation (NSD) using diffusion tensor imaging (DTI).
  • To investigate the correlation between observed OB microstructural changes and the degree of NSD.

Main Methods:

  • Diffusion tensor imaging (DTI) was performed on 96 patients with NSD.
  • Two independent readers assessed DTI data, measuring fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values in the ipsilateral and contralateral OB.
  • Patients were categorized into three groups based on the NSD angle.

Main Results:

  • Significant differences in FA and ADC values were observed between the left and right OBs across the three NSD groups.
  • A negative correlation was found between OB FA values and both age and deviation angle.
  • A positive correlation was observed between OB ADC values and both age and deviation angle.

Conclusions:

  • This study provides the first evidence of increased ADC and decreased FA in the OB of NSD patients, suggesting axonal damage and loss of microstructural integrity.
  • The observed microstructural abnormalities in the OB are directly proportional to the degree of NSD.