Skewness Calculator

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Enter at least three numbers separated by commas or spaces. You need at least four values to compute kurtosis.
Fisher-Pearson G1 corrects the small-sample bias in g1 and matches the Excel SKEW function. Pearson median skewness is a robust alternative that is less sensitive to outliers.
Excess kurtosis (Fisher kurtosis) measures tail-heaviness relative to a normal distribution. Zero means normal-like tails; positive means heavier tails (leptokurtic); negative means lighter tails (platykurtic). Requires at least four values.
Skewness (primary method)Highly right-skewed
1.3281
Directionright-skewed (positive)
Standard error of skewness0.7937
Excess kurtosis (Fisher)3.8373
Standard error of kurtosis1.5875
Fisher-Pearson G11.3281
Moment skewness g11.0247
Pearson median skewness1.429
Mean8
Median5
Sample std deviation (s)6.2981
Sample variance (s²)39.6667
Range18
Count (n)7
1.3281
Highly left-skewed<-1Moderately left-skewed-1--0.5Fairly symmetric-0.5-0.5Moderately right-skewed0.5-1Highly right-skewed1+

Skewness G1 = 1.3281, the data is highly right-skewed.

  • Skewness measures asymmetry: zero means a perfectly balanced shape; positive means a right tail; negative means a left tail.
  • The longer tail points to the right, so a handful of unusually large values pull the mean above the median.
  • The mean (8) is above the median (5), which confirms the skew direction.
  • The standard error of skewness is 0.7937. A rough rule: if |G1| exceeds 2 * SES (1.5875), the skewness is statistically notable for this sample size.
  • Excess kurtosis is 3.8373 (leptokurtic (heavy-tailed)). Positive excess kurtosis suggests more extreme outliers than a normal distribution; negative suggests fewer.

Next stepPlot a histogram to visually confirm the skew, and compare the mean with the median: a noticeable gap confirms the direction.

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