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

### 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.

### 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).