Ronny Kohavi and GrowthBook cover the A/B testing best practices for designing experiments that drive long-term growth.
A team of 4 growth engineers ran 360 experiments in a year, helping Fyxer grow from $1M to $35M ARR. Here's how they combined a growth engineering mindset with AI-powered coding to test at a pace most companies can't match.
Khan Academy went from vibes-based prompt testing to running A/B experiments on their AI tutor, Khanmigo, in production. Kelli Hill shares the journey, including a fascinating iterative case study on latency vs. math accuracy.
Real data from The Social Hub shows just how much you can save with GrowthBook.
Why optimizing for short-term A/B test wins can degrade user trust and product quality. A look at common dark
Only 10–30% of experiments produce a clear winner — and that’s not a problem, it’s reality. This article shows how high-performing teams design experiments to learn faster and make better decisions, even when results are neutral.
That model topping the leaderboards? It might be the worst choice for your app. Here's why benchmarks are lying to you—and how A/B testing reveals what actually works.
GrowthBook provides you with 3 different types of experimentation for different purposes: Bandits for picking a winner among many, Safe Rollouts for releasing safely, and Experiments for learning.
The Experiment Decision Framework helps answers those tricky questions about when to end your experiment and whether the results are up to snuff.
It’s estimated that over 40% of all new code is now written by AI and that trend is likely
Imagine making every product decision with data, powered by the best A/B testing platforms of 2025. These tools have
Discover how Multi-Armed Bandits revolutionize A/B testing in GrowthBook. Learn to optimize experiments automatically, reduce opportunity costs, and make data-driven decisions faster.
In under two minutes, GrowthBook can be set up and ready for feature flagging and A/B testing, whether you use our cloud or self-host.
