Skewness/Kurtosis

Question 1

Normal distribution

Answer 1

Bell curve

Question 2

Skewness

Answer 2

Asymmetry of probability distribution around the mean

The further the value is from zero, the more likely it is that the data are not normally distributed.

Can convert these scores to z-scores by dividing by their standard error.
If the resulting score (when you ignore the minus sign) is greater than 1.96 then it is significant (p < 0.05).

Question 3

Positive Skew

Answer 3

Too many low scores in the distribution

Heavy tail

Question 4

Negative skew

Answer 4

Too many high scores in the distribution

Light tail

Question 5

Kurtosis

Answer 5

Flatness/peakedness of a distribution

Identifies whether the tails of a given distribution contain extreme values.

+-1 or 2

Question 6

Mesokurtic

Answer 6

Normal distribution

Excess kurtosis of 0 or close to 0

Question 7

Positive kurtosis

Answer 7

Flatter than normal

Question 8

Negative kurtosis

Answer 8

More peaked than normal

Question 9

Mean, median, and skew

Answer 9

mean > median = positive skew

mean < median = negative skew