Skewness Calculator

Sharing helps me build more free tools

Skewness measures how asymmetrical a distribution is.

Distributions with skewness close to zero are close to symmetrical. Distributions with skewness further from zero are more asymmetrical.

Distributions with a negative skewness are "left skewed". This means they have a longer tail on the left hand side. Distributions with a positive skewness are "right skewed" and have a longer tail on the right-hand side.

When To Use Skewness

Skewness is useful when you want to measure how symmetric a distribution is. A highly skewed distribution will be more influenced by values on one of the tails than the other.

Calculating skewness and kurtosis will tell you about how symmetrical a distribution is and how much of it is in the tails.

How to use this Online Skewness Calculator

  1. Upload your data set to calculate skewness

    Click the dataset input at the top of the page. Or drag and drop your dataset into the input box.

  2. View the skewness of each column

    A table will appear on the screen with the skewness of each column

Types of Skewness

A distribution can have positive skew, negative skew, or no skew.

Positive skew means that the distribution has more outliers on the right side. It is also known as "right skewed". I

A negative skew means that the distribution has more outliers on the left side. This is also known as "left skew".

A zero skew means that the distribution is symmetrical. The normal distribution has a skewness of zero.

The skewness of a Probability Distribution

A right-skewed probability distribution of a real valued random variable has probabilities that taper off slowly for high values on the right.

A left-skewed probability distribution has probabilities that taper off for low values on the left.

Skewness Formula

Pearson's Median Skewness can be calculated as 3 * (mean - median) / std. Where std is the distribution's standard deviation.

The numerator (mean - median) means that distributions with a mean lower than the median will have negative skewness. And distributions with a mean higher than the median will have positive skewness.

Dividing by the standard deviation normalizes the skewness measure to the overall variation in the data. A distribution with a (mean - median) of 5 and a standard deviation of 100 probably looks fairly symmetrical. While another distribution with a (mean - median) of 5 and a standard deviation of 3 probably looks right skew (a longer tail of numbers on the right-hand side).