A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
Chi-Square Test in Python for Conversion Mock Data
Compute the chi-square statistic and approximate p-value for a mock A/B conversion test using the standard library.
import math
from collections import Counter
def chi_square_statistic(observed):
"""
Compute chi-square statistic for a mock conversion test.
observed: dict mapping outcomes to observed frequencies.
"""
observed = Counter(observed)
n = sum(observed.values())
expected = n / len(observed) if …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…Browse by section
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A/B testing & experimentation — Python code examples
What you will find here
This page collects a/b testing & experimentation snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.