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A/B testing & experimentation

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

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A/B testing & experimentation medium

Bayesian A/B Test Credible Interval in Python

Simulates A/B test data and computes posterior credible intervals and the probability that variant B outperforms A using Bayesian Beta-Binomial inference.

bayesian ab-testing credible-interval
Python 
import numpy as np
from scipy import stats

# Simulated A/B test data
n_A = 1000
n_B = 1000
conversions_A = 120
conversions_B = 140

# Prior: Beta(1, 1) uniform
alpha_prior, beta_prior = 1, 1

# Posterior parameters
alpha_A = alpha_prior + conversions_A
beta_A = beta_prior + n_A - conversions_A
alpha_B = alpha_prior +…
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