Nonskewed

Nonskewed is an adjective used in statistics and data analysis to describe a distribution, sample, or dataset that exhibits little to no skewness. Skewness measures the asymmetry of a distribution around its center; a perfectly symmetric distribution has skewness zero. A nonskewed distribution is therefore approximately symmetric, with tails balanced on both sides of the central value. In practice, “nonskewed” is often used when the skewness statistic is near zero within a predefined tolerance, or when a transformation has reduced asymmetry to an acceptable level.

Common examples of nonskewed distributions are the normal distribution, the uniform distribution, and many symmetric members of the family of error distributions. Real data are rarely perfectly symmetric, so small skewness can be considered nonskewed for practical purposes, while large skewness indicates a departure from symmetry. Measurement noise, outliers, or the underlying process can influence skewness estimates.

Measurement and estimation: Skewness is typically estimated from data using the standardized third moment; sample skewness can be sensitive to outliers and sample size. Tests such as D’Agostino’s K-squared or Bowley’s measure can assess whether skewness is significantly different from zero.

Applications: In statistical modeling, many methods assume or perform best with symmetric (nonskewed) data, including ordinary least squares regression and various parametric tests. Data preprocessing may include transformations (for example, logarithmic or Box-Cox) to achieve a nonskewed shape or symmetry.

Limitations: “Nonskewed” is a heuristic notion rather than a strict classification; definitions of acceptable skewness vary by field and context. See also: skewness, symmetry, data transformation.