-- Standard deviation and variance

SELECT

department,

AVG(salary) AS avg_salary,

STDDEV(salary) AS std_dev,

VARIANCE(salary) AS variance

FROM employees

GROUP BY department;

-- Percentiles (PostgreSQL)

SELECT

department,

PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY salary) AS p25,

PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY salary) AS median,

PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY salary) AS p75

FROM employees

GROUP BY department;

-- Combined statistical analysis

SELECT

department,

COUNT(*) AS employee_count,

AVG(salary) AS avg_salary,

STDDEV(salary) AS std_dev,

MIN(salary) AS min_salary,

MAX(salary) AS max_salary

FROM employees

GROUP BY department;

STDDEV(column) calculates standard deviation

VARIANCE(column) calculates variance

PERCENTILE_CONT for interpolated percentiles

PERCENTILE_DISC for actual data values

WITHIN GROUP (ORDER BY col) required for percentiles

NULL values ignored in calculations

Full support: STDDEV, VARIANCE, PERCENTILE_CONT, PERCENTILE_DISC

Limited: STDDEV, VARIANCE (no percentile functions)

STDEV, VAR, PERCENTILE_CONT, PERCENTILE_DISC

Core references in this topic include WHERE, =, <, >, <=, >=. Learn what each one does, when to use it, and the execution or engine rules that matter.

WHERE

Filters rows before projection and sorting. It decides which rows continue through the query pipeline.

SELECT ... FROM table WHERE condition;

Most performance issues start with a weak WHERE clause or a missing supporting index.