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A/B testing & experimentation

User bucketing, experiment metrics, statistical comparison, and rollout guardrails.

4 matches

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.

statistics p-values multiple-comparisons

Python

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…

16 0 Open

A/B testing & experimentation easy

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.

sample-size statistics ab-testing

Python

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: …

15 0 Open

A/B testing & experimentation easy

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.

unittest mock testing

Python

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(…

15 0 Open

A/B testing & experimentation easy

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.

json mock-data multivariate

Python

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,
          …

13 0 Open

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