A/B Test Analysis in SQL: Calculating Statistical Significance Yourself

Analyze A/B test results with SQL alone. Z-test, confidence intervals, and sample size calculation.

TL;DR

A/B Testing: Compare two versions to determine which performs better
Statistical Significance: Confirm results aren't due to chance (p-value < 0.05)
SQL Calculation: Conversion rates, Z-scores, confidence intervals all possible in SQL
Caution: Consider sample size, MDE, and test duration

1. A/B Testing Basics

Core Concept

sql

Control (A): Original version - 10% conversion rate
Treatment (B): New version - 12% conversion rate

Question: Is B really better, or is it just chance?

Key Metrics

Metric	Description
Conversion Rate	Conversions / Exposures
Lift	Improvement = (B - A) / A
p-value	Probability of chance (< 0.05 = significant)
Confidence Interval	Range where true effect likely lies

2. Basic Table Structure

sql

-- Experiment assignment table
CREATE TABLE experiment_assignments (
    user_id INT,
    experiment_name VARCHAR(100),
    variant VARCHAR(50),  -- 'control' or 'treatment'
    assigned_at TIMESTAMP
);

-- Conversion events table
CREATE TABLE conversions (
    user_id INT,
    conversion_type VARCHAR(100),
    converted_at TIMESTAMP,
    revenue DECIMAL(10, 2)
);

3. Basic Conversion Rate Comparison

Calculate Conversion Rate per Group

sql

WITH experiment_data AS (
    SELECT
        ea.variant,
        COUNT(DISTINCT ea.user_id) as users,
        COUNT(DISTINCT c.user_id) as conversions
    FROM experiment_assignments ea
    LEFT JOIN conversions c
        ON ea.user_id = c.user_id
        AND c.converted_at > ea.assigned_at
        AND c.converted_at < ea.assigned_at + INTERVAL '7 days'
    WHERE ea.experiment_name = 'checkout_button_color'
    GROUP BY ea.variant
)
SELECT
    variant,
    users,
    conversions,
    ROUND(conversions * 100.0 / users, 4) as conversion_rate,
    ROUND(conversions * 1.0 / users, 6) as cr_decimal
FROM experiment_data;

Result:

variant	users	conversions	conversion_rate	cr_decimal
control	10000	1000	10.0000	0.100000
treatment	10000	1150	11.5000	0.115000

4. Z-Test for Significance

Testing Difference Between Two Proportions

sql

WITH experiment_stats AS (
    SELECT
        variant,
        COUNT(DISTINCT ea.user_id) as n,
        COUNT(DISTINCT c.user_id) as x,
        COUNT(DISTINCT c.user_id) * 1.0 / COUNT(DISTINCT ea.user_id) as p
    FROM experiment_assignments ea
    LEFT JOIN conversions c
        ON ea.user_id = c.user_id
        AND c.converted_at > ea.assigned_at
    WHERE ea.experiment_name = 'checkout_button_color'
    GROUP BY ea.variant
),
pooled_stats AS (
    SELECT
        SUM(x) * 1.0 / SUM(n) as p_pooled,
        MAX(CASE WHEN variant = 'control' THEN n END) as n_control,
        MAX(CASE WHEN variant = 'control' THEN p END) as p_control,
        MAX(CASE WHEN variant = 'treatment' THEN n END) as n_treatment,
        MAX(CASE WHEN variant = 'treatment' THEN p END) as p_treatment
    FROM experiment_stats
),
z_calculation AS (
    SELECT
        p_control,
        p_treatment,
        n_control,
        n_treatment,
        p_pooled,
        -- Standard Error
        SQRT(p_pooled * (1 - p_pooled) * (1.0/n_control + 1.0/n_treatment)) as se,
        -- Difference
        p_treatment - p_control as diff
    FROM pooled_stats
)
SELECT
    ROUND(p_control * 100, 2) as control_rate_pct,
    ROUND(p_treatment * 100, 2) as treatment_rate_pct,
    ROUND(diff * 100, 2) as absolute_lift_pct,
    ROUND((diff / p_control) * 100, 2) as relative_lift_pct,
    ROUND(diff / se, 4) as z_score,
    -- p-value approximation (two-tailed)
    CASE
        WHEN ABS(diff / se) > 2.576 THEN '< 0.01 ***'
        WHEN ABS(diff / se) > 1.96 THEN '< 0.05 **'
        WHEN ABS(diff / se) > 1.645 THEN '< 0.10 *'
        ELSE '> 0.10 (not significant)'
    END as p_value_approx
FROM z_calculation;

Result:

control_rate_pct	treatment_rate_pct	absolute_lift_pct	relative_lift_pct	z_score	p_value_approx
10.00	11.50	1.50	15.00	3.51	< 0.01 ***

→ Z-score 3.51 > 2.576, significant at 99% confidence level!

5. Confidence Interval Calculation

Estimate Effect Range

sql

WITH experiment_stats AS (
    SELECT
        variant,
        COUNT(DISTINCT ea.user_id) as n,
        COUNT(DISTINCT c.user_id) * 1.0 / COUNT(DISTINCT ea.user_id) as p
    FROM experiment_assignments ea
    LEFT JOIN conversions c
        ON ea.user_id = c.user_id
        AND c.converted_at > ea.assigned_at
    WHERE ea.experiment_name = 'checkout_button_color'
    GROUP BY ea.variant
),
stats AS (
    SELECT
        MAX(CASE WHEN variant = 'control' THEN n END) as n_c,
        MAX(CASE WHEN variant = 'control' THEN p END) as p_c,
        MAX(CASE WHEN variant = 'treatment' THEN n END) as n_t,
        MAX(CASE WHEN variant = 'treatment' THEN p END) as p_t
    FROM experiment_stats
),
confidence_interval AS (
    SELECT
        p_t - p_c as diff,
        -- SE for difference of two proportions
        SQRT(
            (p_c * (1 - p_c) / n_c) +
            (p_t * (1 - p_t) / n_t)
        ) as se
    FROM stats
)
SELECT
    ROUND(diff * 100, 2) as lift_pct,
    ROUND((diff - 1.96 * se) * 100, 2) as ci_lower_95,
    ROUND((diff + 1.96 * se) * 100, 2) as ci_upper_95,
    ROUND((diff - 2.576 * se) * 100, 2) as ci_lower_99,
    ROUND((diff + 2.576 * se) * 100, 2) as ci_upper_99,
    CASE
        WHEN (diff - 1.96 * se) > 0 THEN 'Positive effect (95% confident)'
        WHEN (diff + 1.96 * se) < 0 THEN 'Negative effect (95% confident)'
        ELSE 'Inconclusive'
    END as conclusion
FROM confidence_interval;

Result:

lift_pct	ci_lower_95	ci_upper_95	ci_lower_99	ci_upper_99	conclusion
1.50	0.66	2.34	0.39	2.61	Positive effect (95% confident)

→ 95% CI: 0.66% to 2.34% (doesn't include 0, so significant)

6. Sample Size Calculation

Required Samples Before Starting Test

sql

-- Parameter setup
WITH params AS (
    SELECT
        0.10 as baseline_rate,     -- Baseline 10%
        0.02 as mde,               -- Minimum detectable effect 2pp
        1.96 as z_alpha,           -- 95% confidence (two-tailed)
        0.84 as z_beta             -- 80% power
),
sample_size AS (
    SELECT
        baseline_rate as p1,
        baseline_rate + mde as p2,
        (baseline_rate + baseline_rate + mde) / 2 as p_avg,
        z_alpha,
        z_beta
    FROM params
)
SELECT
    -- Sample per group formula
    CEIL(
        2 * POWER(
            (z_alpha * SQRT(2 * p_avg * (1 - p_avg)) +
             z_beta * SQRT(p1 * (1 - p1) + p2 * (1 - p2)))
            / (p2 - p1),
            2
        )
    ) as sample_per_group,
    -- Total sample
    2 * CEIL(
        2 * POWER(
            (z_alpha * SQRT(2 * p_avg * (1 - p_avg)) +
             z_beta * SQRT(p1 * (1 - p1) + p2 * (1 - p2)))
            / (p2 - p1),
            2
        )
    ) as total_sample
FROM sample_size;

7. Revenue-Based A/B Test (t-test)

Comparing Average Revenue

sql

WITH revenue_stats AS (
    SELECT
        ea.variant,
        COUNT(DISTINCT ea.user_id) as n,
        AVG(COALESCE(c.revenue, 0)) as mean_revenue,
        STDDEV(COALESCE(c.revenue, 0)) as std_revenue
    FROM experiment_assignments ea
    LEFT JOIN conversions c
        ON ea.user_id = c.user_id
        AND c.converted_at > ea.assigned_at
    WHERE ea.experiment_name = 'pricing_test'
    GROUP BY ea.variant
),
t_test AS (
    SELECT
        MAX(CASE WHEN variant = 'control' THEN mean_revenue END) as mean_c,
        MAX(CASE WHEN variant = 'control' THEN std_revenue END) as std_c,
        MAX(CASE WHEN variant = 'control' THEN n END) as n_c,
        MAX(CASE WHEN variant = 'treatment' THEN mean_revenue END) as mean_t,
        MAX(CASE WHEN variant = 'treatment' THEN std_revenue END) as std_t,
        MAX(CASE WHEN variant = 'treatment' THEN n END) as n_t
    FROM revenue_stats
)
SELECT
    ROUND(mean_c, 2) as control_arpu,
    ROUND(mean_t, 2) as treatment_arpu,
    ROUND(mean_t - mean_c, 2) as difference,
    ROUND((mean_t - mean_c) / mean_c * 100, 2) as lift_pct,
    -- Welch's t-test
    ROUND(
        (mean_t - mean_c) /
        SQRT(POWER(std_c, 2) / n_c + POWER(std_t, 2) / n_t),
        4
    ) as t_statistic,
    CASE
        WHEN ABS((mean_t - mean_c) / SQRT(POWER(std_c, 2) / n_c + POWER(std_t, 2) / n_t)) > 2.576 THEN '< 0.01 ***'
        WHEN ABS((mean_t - mean_c) / SQRT(POWER(std_c, 2) / n_c + POWER(std_t, 2) / n_t)) > 1.96 THEN '< 0.05 **'
        ELSE '> 0.05'
    END as significance
FROM t_test;

8. Segment Analysis

Effect Differences by User Group

sql

WITH segment_results AS (
    SELECT
        u.country,
        ea.variant,
        COUNT(DISTINCT ea.user_id) as users,
        COUNT(DISTINCT c.user_id) as conversions,
        COUNT(DISTINCT c.user_id) * 1.0 / COUNT(DISTINCT ea.user_id) as conversion_rate
    FROM experiment_assignments ea
    JOIN users u ON ea.user_id = u.user_id
    LEFT JOIN conversions c
        ON ea.user_id = c.user_id
        AND c.converted_at > ea.assigned_at
    WHERE ea.experiment_name = 'checkout_button_color'
    GROUP BY u.country, ea.variant
),
segment_comparison AS (
    SELECT
        country,
        MAX(CASE WHEN variant = 'control' THEN conversion_rate END) as cr_control,
        MAX(CASE WHEN variant = 'treatment' THEN conversion_rate END) as cr_treatment,
        MAX(CASE WHEN variant = 'control' THEN users END) as n_control,
        MAX(CASE WHEN variant = 'treatment' THEN users END) as n_treatment
    FROM segment_results
    GROUP BY country
)
SELECT
    country,
    ROUND(cr_control * 100, 2) as control_pct,
    ROUND(cr_treatment * 100, 2) as treatment_pct,
    ROUND((cr_treatment - cr_control) * 100, 2) as lift_pct,
    n_control + n_treatment as total_users,
    CASE
        WHEN n_control + n_treatment < 1000 THEN 'Low sample - inconclusive'
        WHEN cr_treatment > cr_control * 1.1 THEN 'Strong positive'
        WHEN cr_treatment > cr_control THEN 'Slight positive'
        ELSE 'Negative or no effect'
    END as segment_result
FROM segment_comparison
ORDER BY total_users DESC;

9. Time-Based Effect Tracking

Detecting Novelty Effect

sql

WITH daily_results AS (
    SELECT
        DATE(ea.assigned_at) as date,
        ea.variant,
        COUNT(DISTINCT ea.user_id) as users,
        COUNT(DISTINCT c.user_id) as conversions
    FROM experiment_assignments ea
    LEFT JOIN conversions c
        ON ea.user_id = c.user_id
        AND c.converted_at > ea.assigned_at
        AND c.converted_at < ea.assigned_at + INTERVAL '1 day'
    WHERE ea.experiment_name = 'checkout_button_color'
    GROUP BY DATE(ea.assigned_at), ea.variant
)
SELECT
    date,
    MAX(CASE WHEN variant = 'control' THEN conversions * 100.0 / users END) as control_rate,
    MAX(CASE WHEN variant = 'treatment' THEN conversions * 100.0 / users END) as treatment_rate,
    MAX(CASE WHEN variant = 'treatment' THEN conversions * 100.0 / users END) -
    MAX(CASE WHEN variant = 'control' THEN conversions * 100.0 / users END) as daily_lift
FROM daily_results
GROUP BY date
ORDER BY date;

Verify that lift is sustained over time!

10. SRM (Sample Ratio Mismatch) Check

Verify Assignment Ratio is Correct

sql

WITH assignment_counts AS (
    SELECT
        variant,
        COUNT(*) as assigned
    FROM experiment_assignments
    WHERE experiment_name = 'checkout_button_color'
    GROUP BY variant
),
srm_check AS (
    SELECT
        SUM(assigned) as total,
        MAX(CASE WHEN variant = 'control' THEN assigned END) as control,
        MAX(CASE WHEN variant = 'treatment' THEN assigned END) as treatment
    FROM assignment_counts
)
SELECT
    control,
    treatment,
    total,
    ROUND(control * 100.0 / total, 2) as control_pct,
    ROUND(treatment * 100.0 / total, 2) as treatment_pct,
    -- Chi-square test for 50/50 split
    ROUND(
        POWER(control - total * 0.5, 2) / (total * 0.5) +
        POWER(treatment - total * 0.5, 2) / (total * 0.5),
        4
    ) as chi_square,
    CASE
        WHEN POWER(control - total * 0.5, 2) / (total * 0.5) +
             POWER(treatment - total * 0.5, 2) / (total * 0.5) > 3.84
        THEN 'SRM DETECTED - Check implementation!'
        ELSE 'OK - Ratio looks fine'
    END as srm_result
FROM srm_check;

11. Practical Checklist

Before Test

sql

-- 1. Sufficient sample?
SELECT COUNT(*) FROM daily_active_users WHERE date >= CURRENT_DATE - 7;

-- 2. Baseline conversion rate?
SELECT AVG(converted) FROM historical_conversions WHERE date >= CURRENT_DATE - 30;

During Test

sql

-- 3. SRM check
-- 4. Daily trend monitoring

After Test

sql

-- 5. Significance testing
-- 6. Segment analysis
-- 7. Long-term effect tracking

Conclusion

Analysis Step	SQL Key
Conversion Rate	COUNT DISTINCT + LEFT JOIN
Z-Test	Pooled proportion + SE calculation
Confidence Interval	± 1.96 * SE
Sample Size	Formula-based pre-calculation
Segments	GROUP BY segment
SRM Check	Chi-square test

A/B Testing Key Points:

p-value < 0.05 is the minimum bar
If CI includes 0, result is uncertain
Don't conclude with insufficient samples
SRM is a sign of implementation bugs

References

Evan Miller - Sample Size Calculator
Optimizely - Statistics Engine
VWO - A/B Testing Statistical Significance