Aggregate

STDDEV_POP

BigQuery

Menghitung standar deviasi populasi dari nilai numerik. Menggunakan formula pembagi N (bukan N-1), cocok ketika data merepresentasikan seluruh populasi.

Tipe hasil: FLOAT64Diperbarui: 7 Jan 2026

Syntax

SQL

STDDEV_POP(expression)

Parameter

expressionnumericwajib

Kolom atau ekspresi numerik yang akan dihitung standar deviasi populasinya

Contoh Penggunaan

Population vs Sample Standard Deviation

SQL

1 SELECT
2   STDDEV_POP(score) as population_stddev,
3   STDDEV_SAMP(score) as sample_stddev,
4   STDDEV(score) as stddev_alias
5 FROM `project.dataset.exam_scores`;

Membandingkan population dan sample standard deviation.

Hasil

population_stddev	sample_stddev	stddev_alias
12.5	12.8	12.8

STDDEV_POP untuk Seluruh Data Karyawan

SQL

1 SELECT
2   department,
3   AVG(salary) as avg_salary,
4   STDDEV_POP(salary) as salary_stddev_pop
5 FROM `project.dataset.all_employees`
6 GROUP BY department
7 ORDER BY salary_stddev_pop DESC;

Standar deviasi populasi gaji per departemen (semua karyawan).

Hasil

department	avg_salary	salary_stddev_pop
Engineering	18000000	5200000
Sales	12000000	4500000
Operations	9500000	1800000

Variabilitas Sensor Readings

SQL

1 SELECT
2   sensor_id,
3   AVG(reading) as avg_reading,
4   STDDEV_POP(reading) as reading_variability,
5   MIN(reading) as min_reading,
6   MAX(reading) as max_reading
7 FROM `project.dataset.sensor_data`
8 WHERE DATE(timestamp) = CURRENT_DATE()
9 GROUP BY sensor_id
10 ORDER BY reading_variability DESC
11 LIMIT 10;

Menganalisis variabilitas sensor readings (data lengkap).

Hasil

sensor_id	avg_reading	reading_variability	min_reading	max_reading
S001	25.5	3.2	18.0	32.0
S002	22.1	1.8	19.5	26.0

Fungsi Terkait

STDDEVSTDDEV_SAMPVARIANCEVAR_POPVAR_SAMPCORRCOVAR_POPCOVAR_SAMPPERCENTILE_CONTPERCENTILE_DISC

Kembali ke BigQuery Functions

1	SELECT
2	STDDEV_POP(score) as population_stddev,
3	STDDEV_SAMP(score) as sample_stddev,
4	STDDEV(score) as stddev_alias
5	FROM `project.dataset.exam_scores`;

1	SELECT
2	department,
3	AVG(salary) as avg_salary,
4	STDDEV_POP(salary) as salary_stddev_pop
5	FROM `project.dataset.all_employees`
6	GROUP BY department
7	ORDER BY salary_stddev_pop DESC;

1	SELECT
2	sensor_id,
3	AVG(reading) as avg_reading,
4	STDDEV_POP(reading) as reading_variability,
5	MIN(reading) as min_reading,
6	MAX(reading) as max_reading
7	FROM `project.dataset.sensor_data`
8	WHERE DATE(timestamp) = CURRENT_DATE()
9	GROUP BY sensor_id
10	ORDER BY reading_variability DESC
11	LIMIT 10;