# What does high excess kurtosis mean?

## What does high excess kurtosis mean?

Excess kurtosis. Kurtosis measures the “fatness” of the tails of a distribution. Positive excess kurtosis means that distribution has fatter tails than a normal distribution. Fat tails means there is a higher than normal probability of big positive and negative returns realizations.

### What is a good excess kurtosis?

An excess kurtosis above 0 indicates the tails are heavier than the normal distribution. An excess kurtosis below 0 indicates the tails are lighter than the normal distribution. An excess kurtosis value of 1 and above or -1 and below represents a sizable departure from normality.

#### How do you interpret excess kurtosis?

If the kurtosis is greater than 3, then the dataset has heavier tails than a normal distribution (more in the tails). If the kurtosis is less than 3, then the dataset has lighter tails than a normal distribution (less in the tails).

What is excess kurtosis in a data distribution?

Excess kurtosis is a metric that compares the kurtosis of a distribution against the kurtosis of a normal distribution. The kurtosis of a normal distribution equals 3.

Can kurtosis be negative?

In statistics, kurtosis is used to describe the shape of a probability distribution. Specifically, it tells us the degree to which data values cluster in the tails or the peak of a distribution. The kurtosis for a distribution can be negative, equal to zero, or positive.

## What does a negative kurtosis mean?

A distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value.

### What does it mean if kurtosis is negative?

Negative excess values of kurtosis (<3) indicate that a distribution is flat and has thin tails. Platykurtic distributions have negative kurtosis values. A platykurtic distribution is flatter (less peaked) when compared with the normal distribution, with fewer values in its shorter (i.e. lighter and thinner) tails.

#### What does negative and positive kurtosis mean?

This definition is used so that the standard normal distribution has a kurtosis of zero. In addition, with the second definition positive kurtosis indicates a “heavy-tailed” distribution and negative kurtosis indicates a “light tailed” distribution.

What does positive and negative kurtosis indicate?

What causes negative kurtosis?

## What does negative value of kurtosis mean?

Negative kurtosis: A distribution with a negative kurtosis value indicates that the distribution has lighter tails and a flatter peak than the normal distribution. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value. Peter Westfall.

### How to interpret excess kurtosis and skewness?

• Select “descriptive statistics” from the analysis menu.
• From this window,select the variable for which we want to calculate the descriptive statistics and drag them into the variable window.
• #### How do you interpret skewness and kurtosis in SPSS?

Positive (Right) Skewness Example. A scientist has 1,000 people complete some psychological tests.

• Negative (Left) Skewness Example.
• Symmetrical Distribution Implies Zero Skewness.
• Population Skewness – Formula and Calculation.
• Sample Skewness – Formula and Calculation.
• Skewness in SPSS.
• Skewness – Implications for Data Analysis.
• How do you interpret kurtosis?

– Kaplansky I. A Common Error Concerning Kurtosis. Journal of the American Statistical Association. – Ali MM. Stochastic Ordering and Kurtosis Measure. Journal of the American Statistical Association. – Johnson ME, Tietjen GL, Beckman RJ. A New Family of Probability Distributions With Applications to Monte Carlo Studies.

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