# 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 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.

Likewise What if my kurtosis is too high?

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 high is too high for 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). Careful here.

## 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 negative kurtosis good? A distribution with a negative kurtosis value indicates that **the distribution has lighter tails than the normal distribution**. … The solid line shows the normal distribution and the dotted line shows a distribution with a negative kurtosis value.

What is a high level of kurtosis?

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.

What if kurtosis is negative? 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.

## What is the cutoff for kurtosis?

The values for asymmetry and kurtosis **between -2 and +2** are considered acceptable in order to prove normal univariate distribution (George & Mallery, 2010). … (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.

Why is kurtosis a problem? A **higher kurtosis tends to go with more large residuals**, even when you hold the variance constant. [Further, in some cases, the concentration of small residuals may actually lead to more of a problem than the additional fraction of the largest residuals — depending on what things you’re looking at.]

How does kurtosis affect the power of a test?

Likewise, the power of the sign test under the medium-kurtosis parent is uniformly higher than the power for the low-kurtosis parent population. In other words, in the range of kurtosis from 1.8 to 9 the power of the sign test **increases as kurtosis increases**.

Why is skewness bad? A skewed distribution is neither symmetric nor normal because **the data values trail off more sharply on one side than on the other**. … The result is that there are many data values concentrated near zero, and they become systematically fewer and fewer as you move to the right in the histogram.

## Can kurtosis be negative?

The values of **excess kurtosis can be either negative or positive**. When the value of an excess kurtosis is negative, the distribution is called platykurtic. This kind of distribution has a tail that’s thinner than a normal distribution.

What is good skewness and 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 does 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.

How do you interpret a range? Interpreting the Range

The range is interpreted as the **overall dispersion of values in a dataset** or, more literally, as the difference between the largest and the smallest value in a dataset. The range is measured in the same units as the variable of reference and, thus, has a direct interpretation as such.

## What is the difference between positive and negative kurtosis?

So, if a dataset has a positive kurtosis, it has more in the tails than the normal distribution. If a dataset has a negative kurtosis, **it has less in the tails than the normal distribution**.

What does a skewness of 0.5 mean? A skewness value greater than 1 or less than -1 indicates a highly skewed distribution. A value between 0.5 and 1 or -0.5 and -1 is moderately skewed. A value between -0.5 and 0.5 indicates **that the distribution is fairly symmetrical**.

How do you interpret kurtosis values?

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.

How do you interpret kurtosis value? 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).

### How is kurtosis different from skewness?

Skewness is a measure of the degree of lopsidedness in the frequency distribution. Conversely, kurtosis is a measure of degree of tailedness in the frequency distribution. Skewness is an **indicator of lack of symmetry**, i.e. both left and right sides of the curve are unequal, with respect to the central point.

How do you interpret kurtosis results? 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 a Leptokurtic distribution?

Leptokurtic distributions are **variable distributions with wide tails and have positive kurtosis**. In contrast, platykurtic distributions have narrow tails and thus have negative kurtosis, whereas mesokurtic distributions (such as the normal distribution) have a kurtosis of zero.