---
title: Configure a Bayesian A/B test
slug: experimentation-strategy/docs/configure-a-bayesian-a-b-test
docTags: 
createdAt: 2026-08-05T20:33:26.135Z
---

- Optimizely Feature Experimentation
- Optimizely Analytics

Follow these steps to configure a Bayesian A/B test in Optimizely Feature Experimentation. This configuration lets you run an A/B test without a predetermined sample size and a strict analysis plan.

## Prerequisites

Before you begin, you must have the following:

- An [Optimizely Analytics](docId\:S0I_GNXWbK-RSN7nGr-ig) account.
- A flag in Feature Experimentation. See [Manage flags](docId\:j7bgkTxrI8VN_z2dOZu1R).

## When to use a Bayesian analysis

Consider using Bayesian analysis for your A/B tests when you prefer a more intuitive interpretation of results, such as direct probabilities of one variant being better than another.

## Create a Bayesian A/B test rule

### Create an A/B test rule

1. Select a flag from your Feature Experimentation project.
2. Create an A/B test rule in your desired environment. See [Run A/B tests in Feature Experimentation](docId:8pnfCv-sR37goLcxTtCPv).

### Configure your A/B test rule

1. Configure your rule as normal.
2. Select **Bayesian** for the **Stats Configuration**. 

   ![](https://api.archbee.com/api/optimize/BrbvVORKJ5iIeSMZye5kI/4jFUfTM4uitBASHEy0dvi-20260720-144757.png)

:::Paragraph{indent="1"}
This replaces the default Sequential (Optimizely Stats Engine) statistical methods with the Bayesian approach. See [Statistical analysis methods overview](docId\:sF8u1ST65p2LijPMqQ5eQ) for information on the differences between Sequential (Stats Engine), Frequentist (Fixed Horizon) testing, and Bayesian testing.
:::

3. (Optional) Update the** Chance to beat probability threshold**. Must be at least 70%.

## Complete your A/B test rule configuration

1. Configure the remainder of your A/B test rule.
2. Click **Save**.
3. Start your A/B test rule and run your flag to launch your experiment. See [Manage rules](docId\:h1v5aDhM3KPn1_SyZ6e6W).

## Understand Bayesian results

When your Bayesian A/B test is running and collecting data, the results display with specific Bayesian metrics:

- **Credible interval** – A range that contains the true value of the effect with a specified probability.
  - **Example** – If the estimated lift in conversion rate is 3%, and the 95% credible interval is \[1%, 5%], it means there is a 95% chance that the true lift is somewhere between 1% and 5%.
- **Chance to beat** – The probability that a variant performs better than the control.
  - **Example** – If a variant has a 92% chance to beat control, it means there is a 92% probability that its conversion rate is higher than the control's.
- **Probability of being the best arm** – For experiments with multiple variants, this indicates the likelihood that a specific variant is the top performer.
  - **Example** – In a test with three variants, if Variant C has a 75% probability of being the best arm, it means the probability of Variant C having the highest conversion rate among all tested groups is 75%.

By understanding these metrics, you can make informed decisions based on the updated probabilities derived from your experiment data.

## Next steps

For information, see the following documentation:

- [Statistical analysis methods overview](docId\:sF8u1ST65p2LijPMqQ5eQ)
- [Bayesian statistics](docId\:AFuoBBwZn_1m5uHyEcxvl)