Skewness Let me begin by talking about skewness. Skewness characterizes the degree of asymmetry of a distribution around its mean. Positive skewness indicates a distribution with an asymmetric tail extending towards more positive values. While that definition is accurate, it isn't 100 percent helpful because it doesn't explain what the resulting number actually means. The skewness statistic is sometimes also called the skewedness statistic.
Normal distributions produce a skewness statistic of about zero.
So a skewness statistic of -0.01819 would be an acceptable skewness value for a normally distributed set of test scores because it is very close to zero and is probably just a chance fluctuation from zero. Since the sign of the skewness statistic is negative, you know that the distribution is negatively skewed. Alternatively, if the skewness statistic had been positive, you would have known that the distribution was positively skewed. On a norm-referenced test, the existence of positively or negatively skewed distributions as indicated by the skewness statistic is important for you to recognize as a language tester because skewing, one way or the other, will tend to reduce the reliability of the test. Skewed distributions will also create problems insofar as they indicate violations of the assumption of normality that underlies many of the other statistics like correlation coefficients, t-tests, etc. used to study test validity.