# What is kurtosis in statistics with example?

## What is kurtosis in statistics with example?

Kurtosis is a statistical measure used to describe the degree to which scores cluster in the tails or the peak of a frequency distribution. There are three types of kurtosis: mesokurtic, leptokurtic, and platykurtic. Mesokurtic: Distributions that are moderate in breadth and curves with a medium peaked height.

### What does kurtosis value indicate?

Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers.

**How do you calculate kurtosis examples?**

Kurtosis = Fourth Moment / Second Moment2

- Kurtosis = 313209 / (365)2
- Kurtosis = 2.35.

**What does a kurtosis of 3 mean?**

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 acceptable kurtosis?

The values for asymmetry and kurtosis between -2 and +2 are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). Hair et al. (2010) and Bryne (2010) argued that data is considered to be normal if skewness is between ‐2 to +2 and kurtosis is between ‐7 to +7.

### What is the formula for coefficient of kurtosis?

In other words the skewness coefficient measures the departure from symmetry. The normal distribution has skewness equal to zero. The kurtosis of a probability distribution of a random variable x is defined as the ratio of the fourth moment μ4 to the square of the variance σ4, i.e., κ = μ 4 σ 4 −3 .

**What kurtosis is acceptable?**

A standard normal distribution has kurtosis of 3 and is recognized as mesokurtic. An increased kurtosis (>3) can be visualized as a thin “bell” with a high peak whereas a decreased kurtosis corresponds to a broadening of the peak and “thickening” of the tails. Kurtosis >3 is recognized as leptokurtic and <3.

**What is too much kurtosis?**

## What does excess kurtosis mean in statistics?

Unlike skewness, kurtosis measures either tail’s extreme values. Excess kurtosis means the distribution of event outcomes have lots of instances of outlier results, causing fat tails on the bell-shaped distribution curve .

### What is kurtosis and why is it important?

What is Kurtosis? Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of a given distribution contain extreme values.

**What is the difference between normal distribution and kurtosis?**

Normal distribution is a continuous probability distribution wherein values lie in a symmetrical fashion mostly situated around the mean. Kurtosis is a statistical measure used to describe the distribution of observed data around the mean. It is sometimes referred to as the “volatility of volatility.”.

**What is the third classification for kurtosis?**

The third classification for kurtosis is platykurtic. Platykurtic distributions are those that have slender tails. Many times they possess a peak lower than a mesokurtic distribution. The name of these types of distributions come from the meaning of the prefix “platy” meaning “broad.”.