Positive Skew Test Interpretation
A positive skewness value indicates a right-skewed distribution with a longer tail toward higher values. [1] Many “skewness tests” also treat positive skew as mean pulled toward larger observations because the upper tail extends farther from the center than the lower tail. [2]
Distribution Shape Implication
Positive skew corresponds to a histogram or density curve where most values cluster on the lower end and fewer observations extend to the right. [1]
Relationship to Mean and Median
For a right-skewed (positively skewed) distribution, the mean is typically greater than the median. [3]
Practical Consequences in Analysis
Positive skew indicates departures from symmetry that can affect methods assuming approximately symmetric or normal data. [4]
Hypothesis-Testing Context
If the reported result is from a formal normality/skewness test, a “positive skew” outcome indicates the sample is skewed to the right relative to the null (commonly symmetry or normality). [4]
Difference Between “Skewness” and a “Significance” Result
A positive skewness magnitude describes direction and degree of asymmetry. [1] A p-value from the test describes whether the observed skewness differs enough from the null expectation to be unlikely by chance under that model. [4]