What does positive skewness signify in normal distribution?

Answer 1

A normal distribution is not skewed. Skewness is a measure of how the distribution has been pulled away from the normal.

A feature of a distribution is the extent to which it is symmetric.

A perfectly normal curve is symmetric - both sides of the distribution would exactly correspond if the figure was folded across its median point.

It is said to be skewed if the distribution is lop-sided.

The word, skew, comes from derivations associated with avoiding, running away, turning away from the norm.

So skewed to the right, or positively skewed, can be thought of as grabbing the positive end of the bell curve and dragging it to the right, or positive, direction to give it a long tail in the positive direction, with most of the data still concentrated on the left.

Then skewed to the left, or negatively skewed, can be thought of as grabbing the negative end of the bell curve and dragging it to the left, or negative, direction to give it a long tail in the negative direction, with most of the data still bunched together on the right.

Warning: A number of textbooks are not correct in their use of the term 'skew' in relation to skewed distributions, especially when describing 'skewed to the right' or 'skewed to the left'.