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Difference Between Skewness and Kurtosis

Last Updated : 6 Jan, 2026

Skewness and kurtosis are statistical measures that describe the shape of a distribution beyond basic central tendency. Skewness explains how symmetric or asymmetric the data is around its mean, while kurtosis explains how peaked the distribution is and how heavy or light its tails are.

Skewness

Skewness measures the asymmetry of a probability distribution around its mean. It indicates whether data values are stretched more toward the left or the right side, affecting the distribution’s overall balance.

  • Helps understand the direction of extreme values.
  • Influences the difference between mean, median and mode.
  • Useful in identifying required transformations for modelling.

Types of Skewness

👁 mean_mode_median
Types of Skewness
  1. Positive Skewness: It refers to a distribution where the right tail is longer or more stretched than the left. The majority of data points are concentrated on the lower-value side, while a few large values pull the mean to the right.
  2. Negative skewness: It refers to a distribution where the left tail is longer or more stretched than the right. Most data points lie on the higher-value side, while a few small values pull the mean to the left.
  3. Zero Skewness: It represents a perfectly symmetrical distribution, where the left and right sides mirror each other and extreme values occur equally on both sides.

Kurtosis

Kurtosis measures how sharp the peak of a distribution is and how heavy or light its tails are compared to a normal distribution. It shows how much of the data lies close to the center and how much appears in the extreme ends.

  • Higher kurtosis implies more extreme outliers.
  • Lower kurtosis implies fewer extreme values and smoother tails.
  • Excess kurtosis (kurtosis − 3) is commonly used for interpretation.

Types of Kurtosis

👁 distribution
Types of Kurtosis
  1. Mesokurtic: Mesokurtic distributions have a kurtosis value around 3, indicating a moderate peak and tails similar to a normal distribution. They do not show unusually heavy or light tails.
  2. Leptokurtic: A leptokurtic distribution has kurtosis greater than 3. It has a sharp, narrow peak and heavy tails, meaning the data clusters tightly near the mean but also produces more
  3. Platykurtic: A platykurtic distribution has kurtosis less than 3. It features a flatter, wider peak and light tails, indicating fewer extreme values and more evenly spread data.

Skewness vs. Kurtosis

Let's compare skewness and kurtosis,

AspectSkewnessKurtosis
Core PurposeQuantifies the asymmetry of a distributionQuantifies the peakedness and tail weight of a distribution
Primary FocusDirection and degree of horizontal asymmetryVertical structure and extremity of tail behaviour
Positive ValuesReflect a distribution with a longer or heavier right tailReflect a sharper peak with heavier tails and more extreme values
Negative ValuesReflect a distribution with a longer or heavier left tailReflect a flatter peak with lighter tails and fewer extreme values
Effect on Summary StatisticsAlters the relationship among mean, median and modeInfluences tail probability, variance concentration and outlier impact
SensitivitySensitive to directional deviations and skewed clustersHighly sensitive to occurrence and magnitude of extreme observations
Analytical ImplicationUsed to identify directional bias and underlying imbalanceUsed to assess risk, extremity and departure from normal tail behaviour
Role in Distribution ShapeDescribes shape horizontallyDescribes shape vertically and in the tails
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