What does kurtosis represent in statistics?

What does kurtosis represent in statistics?

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 three standard deviations (plus or minus) of the mean.

What are the three types of kurtosis?

Types of Kurtosis

• Mesokurtic. Data that follows a mesokurtic distribution shows an excess kurtosis of zero or close to zero.
• Leptokurtic. Leptokurtic indicates a positive excess kurtosis.
• Platykurtic. A platykurtic distribution shows a negative excess kurtosis.

What is a good kurtosis value?

A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable. Skewness: the extent to which a distribution of values deviates from symmetry around the mean.

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.

What are the different types of kurtosis in statistics?

There are three types of kurtosis: mesokurtic, leptokurtic, and platykurtic.

Why kurtosis is used?

Applications. The sample kurtosis is a useful measure of whether there is a problem with outliers in a data set. Larger kurtosis indicates a more serious outlier problem, and may lead the researcher to choose alternative statistical methods.

What is a kurtosis of 0?

What does it mean when kurtosis is zero? When kurtosis is equal to 0, the distribution is platykurtic. 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 is high kurtosis?

High kurtosis in a data set is an indicator that data has heavy tails or outliers. If there is a high kurtosis, then, we need to investigate why do we have so many outliers. It indicates a lot of things, maybe wrong data entry or other things.

How do you analyze 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).

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).