Lowskew

Lowskew is a term used to describe a data distribution that exhibits low skewness, meaning its shape is close to symmetric around the center. In statistics, skewness measures the asymmetry of a distribution; a distribution with little or no skew has a skewness value near zero. Positive skew indicates a longer tail on the right, while negative skew indicates a longer tail on the left. When a distribution is near zero skew, it is often informally described as having low skew.

Skewness is typically quantified using moments, such as Fisher-Pearson skewness or Pearson’s coefficient. In many practical contexts, values close to zero (for example, within a range like -0.5 to 0.5, though this threshold can vary by field) are interpreted as indicating low skew. However, low skew does not guarantee normality, since kurtosis (the heaviness of tails) and other distributional features may still differ from the normal model.

In data analysis and modeling, low skew can simplify assumptions underlying parametric methods, such as linear regression or t-tests, which often perform best when data are approximately symmetric. When skewness is substantial, data transformations (e.g., log, square root, or Box-Cox) or nonparametric approaches may be considered to mitigate asymmetry. Nonetheless, the term lowskew is somewhat informal and context-dependent; it describes a trait of the distribution rather than a strict classification.

See also: skewness, symmetry, normal distribution, transformation, Box-Cox.