For example, let's say we want to know the proportion of subjects that score below a subject who has a score that is one SD above the mean in a normal distribution. Departures from normality occur when the distribution is asymmetric, or spread out on one end. Distributions that have a large number of observations at lower score levels, and fewer observations that trail off toward the higher score levels are skewed to the right. In contrast, distributions that have a relatively larger number of subjects at higher scores levels, and fewer observations at lower score levels, are skewed to the left. For example, a test that is very easy for a sample of subjects results in a distribution that is skewed to the left.
Researchers believe that most psychological traits are normally distributed in the population.
One important implication of all of this is that it is always very useful to compare the shape of our observed distribution to the normal distribution. The distribution of responses to this question is described as being negatively skewed. The amount of skewness is often described with adjectives such as slightly skewed, moderately skewed, or highly skewed. Using SPSS to Evaluate Whether a Distribution Appears Normal SPSS can superimpose a normal density curve on top of a histogram plotted for a set of observed scores. Select the Statistics menu.