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Skewness does the job of measuring the symmetry of a distribution. It measures the divergence of a provided distribution from a symmetric distribution, called the normal distribution. On the other hand, the job of kurtosis is determining the distribution tails’ heaviness.

If a set of nearly normal data gets graphed through a histogram then it reflects a bell peak. Students are required to complete Statistics assignments are provided for assessing their knowledge. And so, it remains the motive of students to complete them flawlessly. In this context, they prefer to take benefit of best Australian experts of BookMyEssay for getting the best Skewness and Kurtosis assignment help.

What is called Skewness?

When the values of a particular independent feature of a variable are skewed then based on the sculpt, skewness does violate model assumptions. Again, it might also lessen the interpretation of feature importance. The normal distribution helps know skewness.


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If people discuss normal distribution, then data tends to be symmetrically distributed. Every symmetrical distribution has got a 0 skewness because every measure of a central tendency does lie in the center.

Again, if data tend to be symmetrically distributed, then the right-hand side and the left-hand side comprise the same quantity of observations. When the dataset has got ninety values, then the right-hand side will have forty-five observations and the left-hand side will have forty-five observations. Contrarily, if the data isn’t distributed symmetrically, then that data is known as asymmetrical data, and skewness is observed.

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Getting Introduced to Kurtosis

Similar to skewness, kurtosis is considered a statistical measure that is utilized for describing distribution. The job of skewness is differentiating extreme values in one vs. the other tail but kurtosis does measure extreme values that happen in either tail. A distribution with big kurtosis exhibits tail data that exceeds the tails of a normal distribution.

Distributions that have low kurtosis display tail data which is commonly lesser extreme compared to the tails of a normal distribution. For an investor, high kurtosis suggests that the investor would come across occasional extreme returns and it can be either negative or positive.

The Presence of Skewness

Skewness does exist in the majority of financial markets. It is also a vital measure of risk that is not subsumed by SMB or HML. This is still unclear why skewness exists although many compelling arguments are made that include price discovery, bad or good news asymmetry, uncertainly of information and prospect theory.

Negative skew can receive upper expected returns. Commonly, it is believed that an investor has a preference for constructive skew although evidence that supports a predilection for a negative skew exists too.

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Types of Kurtosis

  • Mesokurtic – When the kurtosis of data is equal to zero or close to zero, then it is known as mesokurtic. It means the data set does follow a normal distribution.
  • Leptokurtic – If kurtosis is more than zero, then the data does fall under leptokurtic.
  • Platykurtic – If the kurtosis is negative or less than zero, then it is called platykurtic.

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