A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
How to Build a Simple Binary Protocol Parser Mock in Python
Defines a mock binary protocol with field definitions, encoding, and decoding to simulate network packet parsing for A/B testing and experiment setup.
class SimpleProtocol:
def __init__(self, name, version):
self.name = name
self.version = version
self.fields = []
def add_field(self, field_name, field_size):
self.fields.append((field_name, field_size))
def parse(self, data):
if len(data) != sum(size for _, size i…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 Hash a User ID to an Experiment Bucket in Python
Deterministically map a user ID to one of N experiment buckets using MD5 hashing and modulo arithmetic.
import hashlib
def hash_to_bucket(user_id: str, num_buckets: int = 10) -> int:
"""Deterministically map a user_id to a bucket (0 to num_buckets-1)."""
digest = hashlib.md5(user_id.encode("utf-8")).hexdigest()
return int(digest[:8], 16) % num_buckets
if __name__ == "__main__":
# Mock experiment: split…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)
res…How to create a global control holdout group in Python
This code implements a deterministic global control holdout group, randomly selecting a fraction of users to be excluded from feature rollouts for experiment validation.
import random
class GlobalControl:
def __init__(self, population_size, holdout_fraction=0.2, seed=42):
random.seed(seed)
self.population_size = population_size
self.holdout_fraction = holdout_fraction
self.holdout_size = int(population_size * holdout_fraction)
self.holdout_…How to hash user IDs to experiment buckets in Python
Deterministically map a user ID to an experiment bucket using MD5 hashing, ensuring stable and consistent assignment for A/B testing.
import hashlib
def hash_user_to_bucket(user_id: str, num_buckets: int = 10) -> int:
"""Deterministically map a user ID to an experiment bucket (0..num_buckets-1)."""
digest = hashlib.md5(user_id.encode("utf-8")).hexdigest()
return int(digest, 16) % num_buckets
if __name__ == "__main__":
mock_users …Simulate a Ramp Rollout Percentage in Python
Simulates a percentage-based ramp rollout with deterministic seeding, returning success/failure/in-progress counts for a mock user population.
import random
from enum import Enum
class RolloutStatus(Enum):
SUCCESS = "success"
FAILED = "failed"
IN_PROGRESS = "in_progress"
def simulate_ramp_rollout(total_users: int, percentage: int, seed: int = 42) -> dict:
"""
Simulates a mock ramp rollout for a given percentage of users.
Returns sta…Browse by section
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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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