A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
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 Evaluate Feature Flags in Python
A Python function that evaluates boolean feature flags with user-specific overrides, returning whether a flag is enabled and the reason for the decision.
import json
def evaluate_feature_flag(feature_name, context, flag_configs):
"""
Evaluates a boolean feature flag given a context dictionary.
Args:
feature_name: The name of the feature flag.
context: A dictionary of user/request context (e.g., {"user_id": "123"}).
flag_configs: A …How to Mock Stratified Assignment by Segment in Python
Simulate stratified assignment for A/B experiments by sampling a fixed proportion of units from each segment, with deterministic seeds for reproducibility.
import random
def stratified_assignment(segments, seed=None):
"""
Mock stratified assignment: given a dict of segment -> population size,
return a dict of segment -> sampled unit ids (deterministic with seed).
"""
if seed is not None:
random.seed(seed)
rng = random.Random(seed)
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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.