A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
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.
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 +…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
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A/B testing & experimentation — Python code examples
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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.
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