How do you explain right skew vs left skew?

[Voila]

I’m studying statistics and need help understanding right skew vs left skew. Can someone explain with a simple example and quick rule?

 

[Aurora]

A right (positive) skew means most values are on the left, with a long tail stretching to the right. A left (negative) skew means most values are on the right, with a long tail stretching to the left.

 

[MitchelThe]

Oh, I had the same problem! Think of right skew as a tail on the right side (high values are stretched) and left skew as a tail on the left (low values stretched). Example: income data is usually right skewed because a few people earn a ton more than most. Quick rule: tail points in the direction of the skew.

 

[Peter]

Right skew means the data has a long tail on the right side, with most values being smaller and a few very large outliers, so the mean is usually greater than the median. Left skew means the data has a long tail on the left side, with most values being larger and a few very small outliers, so the mean is usually less than the median.