A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
Benjamini Hochberg FDR Correction in Python
Implement the Benjamini-HHochberg false discovery rate (FDR) procedure in Python to control the expected proportion of false positives among rejected hypotheses.
import numpy as np
def benjamini_hochberg(p_values, alpha=0.05):
p_values = np.array(p_values)
n = len(p_values)
sorted_idx = np.argsort(p_values)
sorted_p = p_values[sorted_idx]
thresholds = (np.arange(1, n + 1) / n) * alpha
significant = sorted_p <= thresholds
if not significan…Bonferroni Correction in Python
Applies the Bonferroni correction to a list of p-values to control the family-wise error rate when performing multiple comparisons.
import numpy as np
def bonferroni_correction(p_values, alpha=0.05):
"""Apply Bonferroni correction to a list of p-values."""
n = len(p_values)
corrected_alpha = alpha / n
significant = [p < corrected_alpha for p in p_values]
return corrected_alpha, significant
if __name__ == "__main__":
# Moc…Check Covariate Balance in Python
Compute standardized mean differences and KS tests to check covariate balance between treatment and control groups in Python.
import numpy as np
from scipy import stats
def balance_check(treatment, covariate):
"""Check covariate balance between treatment and control groups."""
treat_vals = covariate[treatment == 1]
control_vals = covariate[treatment == 0]
# Standardized mean difference
pooled_std = np.sqrt((np.var(t…How to Calculate Minimum Sample Size for a T-Test in Python
Compute the minimum sample size per group for a two-sample t-test using effect size, significance level, and statistical power.
import math
from scipy.stats import norm
def min_sample_size(effect_size, alpha=0.05, power=0.8):
"""
Calculate minimum sample size for a two-sample t-test (equal groups).
Args:
effect_size: Cohen's d (standardized mean difference)
alpha: significance level (Type I error)
power: …How to Compute Mann-Whitney U Test in Python
Compute the Mann-Whitney U statistic and p-value manually in Python with tie correction and a normal approximation for independent samples.
import numpy as np
from scipy import stats
def mann_whitney_u_mock(sample_a, sample_b):
"""Compute Mann-Whitney U and p-value manually."""
# Combine and rank
combined = sample_a + sample_b
n_a, n_b = len(sample_a), len(sample_b)
n_total = n_a + n_b
# Rank with ties handling (average ranks…How to Conduct a Two-Sample T-Test in Python
Performs Welch's t-test for two independent samples, computing the t-statistic, degrees of freedom, and p-value using NumPy and SciPy.
import numpy as np
def two_sample_t_test(sample1, sample2):
"""Perform Welch's t-test for two independent samples."""
n1, n2 = len(sample1), len(sample2)
mean1, mean2 = np.mean(sample1), np.mean(sample2)
var1, var2 = np.var(sample1, ddof=1), np.var(sample2, ddof=1)
# Standard error of difference
…How to Create a Sticky Consistent Mock with unittest.mock in Python
Shows how to use unittest.mock.patch.object to mock a method consistently across multiple calls, returning a sticky value every time.
from unittest.mock import patch
class Database:
def fetch(self, key):
return f"real value for {key}"
def get_value(db, key):
return db.fetch(key)
if __name__ == "__main__":
db = Database()
with patch.object(db, "fetch", return_value="sticky value") as mock_fetch:
result1 = get_value(…How to Generate Multivariate JSON Mock Data in Python
This script generates mock multivariate JSON-compatible data with measurements and boolean flags for testing and experimentation pipelines.
import json
def multivariate_mock(row_count: int = 3) -> list:
"""Generate mock multivariate data as list of JSON-compatible dicts."""
records = []
for i in range(row_count):
record = {
"id": i + 1,
"measurements": {
"temperature": 20.5 + i * 1.5,
…How to Perform Intent-to-Treat Analysis in Python
Runs an intent-to-treat analysis on mock A/B test data, comparing outcomes by initial group assignment with a t-test for significance.
import pandas as pd
import numpy as np
def intent_to_treat_analysis(data):
"""Perform intent-to-treat (ITT) analysis.
ITT compares outcomes based on initial treatment assignment,
regardless of whether participants actually received the treatment.
"""
# Create a copy to avoid mutating the origina…Thompson Sampling Mock Bandit in Python
Implement a Thompson sampling multi-armed bandit to explore and exploit reward probabilities across multiple options, updating Beta distributions over time.
import random
class ThompsonSamplingBandit:
def __init__(self, num_arms, alpha=1.0, beta=1.0):
self.num_arms = num_arms
self.alpha = [alpha] * num_arms
self.beta = [beta] * num_arms
def select_arm(self):
samples = [random.betavariate(a, b) for a, b in zip(self.alpha, self.beta…Browse by section
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A/B testing & experimentation — Python code examples
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