A/B Testing - HelpMeTest

A/B Testing

A/B Test Experiment Design: How to Set Up Tests That Produce Reliable Results

The failure mode for most A/B tests isn't statistical — it's design. Teams write bad hypotheses, measure the wrong metrics, run tests too short, or contaminate the test population. By the time they realize the error, weeks of data are worthless. Good A/B test design

Testing

A/B Testing vs Canary Deployments: When to Use Each

A/B testing and canary deployments both route subsets of users to different versions of your software. Both involve traffic splitting. Both let you compare two variants. But they solve fundamentally different problems and require different tooling, team involvement, and success criteria. Confusing the two leads to misapplied techniques: running

Testing

LaunchDarkly vs Statsig vs Unleash: Feature Flag Tools Compared (2026)

LaunchDarkly is the enterprise standard with the most complete SDK ecosystem. Statsig combines feature flags with built-in A/B experimentation and product analytics. Unleash is the open-source option you self-host. This comparison covers SDK quality, testing support, pricing model, and the tradeoffs for teams of different sizes. Why the Tool

Testing

GrowthBook Tutorial: Open-Source A/B Testing with Jest and Playwright

GrowthBook is an open-source A/B testing and feature flag platform with a JavaScript SDK that works in both Node.js and browsers. This tutorial covers SDK setup, writing Jest unit tests for flag evaluation, Playwright tests for UI experiment variants, and setting up GrowthBook in CI without a live

Testing

A/B Testing in Production: Statistical Significance, Sample Size, and Rollout Testing

A/B testing infrastructure is code, and code needs tests. This guide covers testing the experiment layer itself: variant assignment consistency, statistical significance calculations, minimum detectable effect, sample size requirements, and percentage rollout correctness—not just whether the feature exists behind the flag. Testing the Test Infrastructure Most teams write

Machine Learning

A/B Testing ML Models in Production: Shadow Mode and Canary Deployments

Offline evaluation tells you a model performs better on held-out data. Production A/B testing tells you it actually improves the business metric you care about. These are different questions, and sometimes they have different answers. A new recommendation model might have higher offline AUC but lower click-through rate in