How do you interpret 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 an acceptable kurtosis value?
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 a high kurtosis score?
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.
What does a positive kurtosis mean?
What does it mean when kurtosis is positive? Positive excess values of kurtosis (>3) indicate that a distribution is peaked and possess thick tails. An extreme positive kurtosis indicates a distribution where more of the values are located in the tails of the distribution rather than around the mean.
How do you interpret skewness and kurtosis in SPSS?
For skewness, if the value is greater than + 1.0, the distribution is right skewed. If the value is less than -1.0, the distribution is left skewed. For kurtosis, if the value is greater than + 1.0, the distribution is leptokurtik. If the value is less than -1.0, the distribution is platykurtik.
What is a bad kurtosis?
A negative kurtosis means that your distribution is flatter than a normal curve with the same mean and standard deviation. This means your distribution is platykurtic or flatter as compared with normal distribution with the same M and SD. The curve would have very light tails.
Why skewness and kurtosis are used?
“Skewness essentially measures the symmetry of the distribution, while kurtosis determines the heaviness of the distribution tails.” The understanding shape of data is a crucial action. It helps to understand where the most information is lying and analyze the outliers in a given data.
Do you want high or low kurtosis?
The risk that does occur happens within a moderate range, and there is little risk in the tails. Alternatively, the higher the kurtosis, the more it indicates that the overall risk of an investment is driven by a few extreme “surprises” in the tails of the distribution.
Is positive kurtosis good?
What does it mean when kurtosis is positive? Positive values of kurtosis indicate that a distribution is peaked and possess thick tails. A leptokurtic distribution has a higher peak and taller (i.e. fatter and heavy) tails than a normal distribution.
What does negative kurtosis tell us?
A negative kurtosis means that your distribution is flatter than a normal curve with the same mean and standard deviation. The easiest way to visualise this is to plot a histogram with a fitted normal curve.
How do you interprete kurtosis and skewness value in SPSS?
In SPSS, the skewness and kurtosis statistic values should be less than ± 1.0 to be considered normal. For skewness, if the value is greater than + 1.0, the distribution is right skewed. If the value is less than -1.0, the distribution is left skewed.
How does kurtosis relate to heaviness of a distribution?
Kurtosis – Kurtosis is a measure of the heaviness of the tails of a distribution. In SAS, a normal distribution has kurtosis 0. Furthermore, how do you interpret descriptive statistics in SPSS?
What’s the difference between skewness and kurtosis?
Skewness essentially measures the relative size of the two tails. Kurtosis is a measure of the combined sizes of the two tails. It measures the amount of probability in the tails.
What does greater than 1 mean in kurtosis?
Greater than 1 means skewed to the right, less than -1 means skewed to the left and therefore deviates significantly from normal. For kurtosis, greater than 1 means peaked (leptokurtic), less than -1 means too flat (platykurtic) and therefore deviates significantly from a normal distribution. See the answer in the attachment.