But lack of skewness alone doesn't imply normality. By drawing a line down the middle of this histogram of normal data it's easy to see that the two sides mirror one another.
Figure A shows normally distributed data, which by definition exhibits relatively little skewness. The solid line shows the normal distribution and the dotted line shows a distribution that has a negative kurtosis value.Īs data becomes more symmetrical, its skewness value approaches zero. For example, data that follow a beta distribution with first and second shape parameters equal to 2 have a negative kurtosis value. Negative kurtosisĪ distribution with a negative kurtosis value indicates that the distribution has lighter tails than the normal distribution. The solid line shows the normal distribution, and the dotted line shows a distribution that has a positive kurtosis value. For example, data that follow a t-distribution have a positive kurtosis value. Positive kurtosisĪ distribution that has a positive kurtosis value indicates that the distribution has heavier tails than the normal distribution. A kurtosis value that significantly deviates from 0 may indicate that the data are not normally distributed. A kurtosis value of 0 indicates that the data follow the normal distribution perfectly.
Normally distributed data establish the baseline for kurtosis.
Use kurtosis to initially understand general characteristics about the distribution of your data.
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