# Quick Answer: What Kurtosis Means?

## Is kurtosis a percentage?

In general, kurtosis tells you nothing about the “peak” of a distribution, and also tells you nothing about its “shoulders.” It measures outliers (tails) only.

For an outlier-prone (heavy tailed) distribution, this percentage is typically higher, like 2.0%..

## What is meant by kurtosis?

Kurtosis is a measure of the combined weight of a distribution’s tails relative to the center of the distribution. When a set of approximately normal data is graphed via a histogram, it shows a bell peak and most data within + or – three standard deviations of the mean.

## Why kurtosis of normal distribution is 3?

The sample kurtosis is correspondingly related to the mean fourth power of a standardized set of sample values (in some cases it is scaled by a factor that goes to 1 in large samples). As you note, this fourth standardized moment is 3 in the case of a normal random variable.

## What is the normal range for kurtosis?

Both skew and kurtosis can be analyzed through descriptive statistics. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 when utilizing SEM (Brown, 2006).

## What is kurtosis 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. The peak is the tallest part of the distribution, and the tails are the ends of the distribution. There are three types of kurtosis: mesokurtic, leptokurtic, and platykurtic.

## How do you solve kurtosis?

x̅ is the mean and n is the sample size, as usual. m4 is called the fourth moment of the data set. m2 is the variance, the square of the standard deviation. The kurtosis can also be computed as a4 = the average value of z4, where z is the familiar z-score, z = (x−x̅)/σ.

## What does excess kurtosis mean?

Excess kurtosis means the distribution of event outcomes have lots of instances of outlier results, causing fat tails on the bell-shaped distribution curve. Normal distributions have a kurtosis of three. Excess kurtosis can, therefore, be calculated by subtracting kurtosis by three.

## How do you interpret kurtosis?

For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked. Likewise, a kurtosis of less than –1 indicates a distribution that is too flat. Distributions exhibiting skewness and/or kurtosis that exceed these guidelines are considered nonnormal.” (Hair et al., 2017, p.

## Is high kurtosis good or bad?

Kurtosis is only useful when used in conjunction with standard deviation. It is possible that an investment might have a high kurtosis (bad), but the overall standard deviation is low (good). Conversely, one might see an investment with a low kurtosis (good), but the overall standard deviation is high (bad).

## What causes high kurtosis?

A high kurtosis is more often caused by processes that directly contribute to a high peak, than by processes that directly contribute to fat tails.

## Is kurtosis a problem?

Most often, kurtosis is measured against the normal distribution. … If the kurtosis is greater than zero, then the distribution has heavier tails and is called a leptokurtic distribution. The problem with both skewness and kurtosis is the impact of sample size.

## What does a negative kurtosis value 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.

## How do you interpret kurtosis in SPSS?

Kurtosis: a measure of the “peakedness” or “flatness” of a distribution. A kurtosis value near zero indicates a shape close to normal. A negative value indicates a distribution which is more peaked than normal, and a positive kurtosis indicates a shape flatter than normal.