SKEWNESS and KURTOSIS: Statistical Functions Come to Oracle SQL

Oracle 23ai adds four new statistical aggregate functions that give SQL developers direct access to distribution shape analysis without leaving the database.

SKEWNESS_POP and SKEWNESS_SAMP

Skewness measures the asymmetry of a distribution around its mean.

• Positive skew: long tail on the right (e.g., most employees earn average salaries, but a few earn very high)
• Negative skew: long tail on the left
• Zero: symmetric distribution

SELECT
    department_id,
    ROUND(SKEWNESS_POP(salary), 4)  AS pop_skewness,
    ROUND(SKEWNESS_SAMP(salary), 4) AS sample_skewness,
    COUNT(*) AS headcount
FROM employees
GROUP BY department_id
ORDER BY ABS(SKEWNESS_SAMP(salary)) DESC;

Use SKEWNESS_POP when you have the full population. Use SKEWNESS_SAMP when working with a sample (applies Bessel’s correction).

KURTOSIS_POP and KURTOSIS_SAMP

Kurtosis measures the “tailedness” of a distribution — whether extreme values are frequent or rare compared to a normal distribution.

SELECT
    product_category,
    ROUND(KURTOSIS_SAMP(order_value), 4) AS kurtosis,
    STDDEV(order_value)                  AS std_deviation,
    AVG(order_value)                     AS mean_value
FROM orders
GROUP BY product_category
HAVING COUNT(*) > 100;  -- meaningful only with sufficient data

High kurtosis means more extreme outliers than expected. This is important for financial risk modeling, anomaly detection, and capacity planning.

Before 23ai: You had to calculate skewness and kurtosis manually using complex expressions involving SUM(POWER(x - mean, 3)) and normalization factors. Or export data to Python/R. Now it’s a single function call in SQL, composable with GROUP BY and window functions.