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A/B testing & experimentation

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

3 matches

How to Do Random Assignment in Python for A/B Tests

Assign each item to a binary group (0 or 1) with uniform probability using a small reusable function, optionally weighted, for A/B testing mocks.

random ab-testing assignment

Python

import random

def random_assignment_uniform_mock(items, weights=None):
    """Assign each item to a group (0 or 1) with uniform probability."""
    if weights is None:
        # Default: each item independently gets 0 or 1 with 50% probability
        return [random.randint(0, 1) for _ in items]
    # Optional weight…

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A/B testing & experimentation easy

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.

ab-testing sampling random

Python

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…

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A/B testing & experimentation easy

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.

hashing ab-testing experiments

Python

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 …

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