Understanding Variance and Kurtosis: A Brief Explanation

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In summary, the difference between variance and kurtosis is that variance measures the dispersion of data while kurtosis measures the "fatness" of the tails in a distribution. High kurtosis means the tails of the distribution are fatter than the normal distribution, while low variance means the tails start off thinner than the normal distribution but do not get as thin as quickly.
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member 428835
hello again pf!

as a really simple question, can someone talk to me about the difference between variance and kurtosis? i know as kurtosis decreases from 3 (normal distribution) our pdf is shorter and fatter, with less weight in the tails. i also know variance tells us how dispersed data is.

but, how to these compare?

please, can someone explain what high kurtosis and low variance means?

thanks!

josh
 
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High kurtosis => terms of (x-μ)4 tend to be large
Low variance => terms of (x-μ)2 tend to be small

Since the 4'th power is more important than the 2'nd power when abs(x-μ) is large, this implies that out towards the tails on both sides, the tails are fatter (higher probability) than the typical PDF with that variance. And the fact that the variance is small implies that the tails start a little thinner than average.

So the conclusion is: Thinner tails than average (normal?) near the mean, and fatter tails than average farther away from the mean -- but not necessarily fatter than they were near the mean, just not getting thin as fast as the normal distribution would.
 
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thanks a ton!
 

1. What is variance?

Variance is a measure of how spread out a set of data is from its mean (average) value. It is calculated by taking the average of the squared differences between each data point and the mean.

2. How is variance different from standard deviation?

Variance and standard deviation are both measures of the spread of data, but variance is the average of the squared differences from the mean while standard deviation is the square root of the variance. Standard deviation is often used instead of variance because it is in the same units as the original data.

3. What is kurtosis?

Kurtosis is a measure of the shape of a distribution or the "peakedness" of a curve. It tells us how much of the data is concentrated near the mean compared to the tails of the distribution.

4. How do we interpret kurtosis?

Kurtosis can have positive or negative values. Positive kurtosis (or leptokurtic distribution) means that the data has a sharper peak and heavier tails compared to a normal distribution. Negative kurtosis (or platykurtic distribution) means that the data has a flatter peak and lighter tails. A value of 0 indicates a normal distribution.

5. Why is it important to understand variance and kurtosis?

Understanding variance and kurtosis can help us better understand and interpret data. It can also help us identify any outliers or extreme values in the data. Additionally, these measures are often used in statistical analyses and can provide important insights into the underlying distribution of the data.

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