Skew
Skewness is a measure of the direction and degree of asymmetry in a probability distribution or observed data around its center.
Effective Data Analysis
Skewness is the degree and direction to which a distribution is asymmetric around its center.
- How it is used
- Uses skewness to assess distribution shape alongside central tendency and variability before inferential testing.
- How deep it goes
- Treats skewness as a normality measure and links it to summary-statistic interpretation, but does not develop its calculation.
- Examples
- Uses contrasting distribution figures and mean–median comparisons involving skewed data and an extreme value.
- What you can do after
- Evaluate distribution shape, interpret means and medians cautiously, and identify when normality assumptions require attention.
Modern C
Skew is a statistical measure of the asymmetry or lopsidedness of a distribution.
- How it is used
- Supplements repeated run-time measurements with a measure of distribution asymmetry.
- How deep it goes
- Mentions skew among timing statistics and shows third-moment collection, but gives no skew calculation or sample value.
- Examples
- Uses 20 timings from 0.1 to 1.3 seconds and a frequency histogram, without calculating their skew.
- What you can do after
- Evaluate/understand skew as a possible check on asymmetry in repeated timing results.
Modern C, Third Edition
Skew measures the asymmetry or lopsidedness of a distribution around its mean.
- How it is used
- Characterizes asymmetry in repeated timing measurements when assessing their variation and reliability.
- How deep it goes
- Defines skew as asymmetry and places it among timing statistics without giving a formula or calculating it for the sample.
- Examples
- Uses 20 fictive timings, a frequency histogram, and a comparison table showing 9% overhead from adding skew collection.
- What you can do after
- Evaluate/understand it as one descriptor of lopsided timings while accounting for the cost of collecting statistics.
Statistics Slam Dunk
Skewness is the asymmetry of a probability distribution around its center.
- How it is used
- Assesses distribution asymmetry during exploratory data analysis and supports decisions about normality.
- How deep it goes
- Defines directional interpretation, gives non-normality thresholds, and pairs the metric with density plots and the Shapiro-Wilk test.
- Examples
- Uses basketball points, spreads, totals, salaries, win shares, and ages as concrete distributions to inspect.
- What you can do after
- Evaluate distribution shape with tableone summaries, density plots, and directional skewness thresholds.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A probability or empirical distribution that is asymmetric around its central tendency.
- How it is used
- Uses distribution checks to identify extreme values and understand regression targets before modeling.
- How deep it goes
- Defines right skew and demonstrates diagnosis with summary statistics, percentiles, filtering, and density visualization.
- Examples
- Uses accommodation prices, percentile output, Boolean range selection, and a kernel density plot with a median marker.
- What you can do after
- Diagnose skew and extreme target values with summary statistics, percentiles, Boolean filtering, and density plots.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains skew →
Related concepts
- symmetrymeasures · Symmetry is the preservation of relevant structure or properties under a specified transformation, such as reflection, exchange, or reversal.
- log transformationaddressed by · A log transformation maps numerical values through a logarithmic function, changing their scale and often compressing large values or converting multiplicative relationships to additive ones.
- Gini coefficientcontrasts with · A Gini coefficient is a summary measure of inequality derived from the area between a Lorenz curve and the line of perfect equality, typically ranging from zero for complete equality toward one for greater inequality.
- density plotmeasured by · A density plot is a visualization that estimates and displays the shape of a continuous variable’s probability distribution as a smooth curve.
- raw momentproduced by · A raw moment is a statistical quantity formed from a specified power of observations measured relative to zero, typically by summing or averaging those powers.
- right-skewed distributionmeasures · A right-skewed distribution has most probability mass or observations at lower values and a comparatively long tail toward higher values.
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