Introduction to Experiments

The Experimentation Module in Loops empowers you to measure the impact of your product changes with confidence. Whether you are running a traditional A/B test or can't , Loops' got your back.

A/B Testing with Bayesian Analysis

When running an A/B test, Loops helps you quickly understand the impact of your experiment using our Bayesian-based statistical engine. This approach provides:

• Faster Insights – Get real-time updates on your test’s progress without waiting for fixed sample sizes.
• Higher Confidence – Bayesian methods offer more intuitive probability-based conclusions, helping you make decisions with clarity.
• Adaptive Learning – Monitor the test dynamically and adjust strategies based on evolving data.

Release Impact

If you are unable to run an A/B test due to technical or business constraints, Loops provides a powerful alternative. Our Release Impact - causal inference model simulate an A/B test using observational data and helps you to measure the effect even if randomize assigment is not possible.

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