Robust Dispersion Measures in the Presence of Outliers
When data is contaminated by outliers, non-robust measures of dispersion, such as standard deviation, do not reflect the dispersion of the uncontaminated data. Robust measures like trimmed variance or 
dispersion give a more reliable result.
You can model contaminated data by adding to the original sample a small normal component with high variance. Then analyze the dispersion.
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Visualize the reliability of these measures by performing a Monte Carlo simulation.
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For data without outliers, 
dispersion is close to the standard deviation.
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