BAYESIAN A/B TESTING

Leave behind p-values and statistical jargon. Focus on simple, meaningful probabilities that drive confident decisions.

Why is it better than classical A/B testing?

There are three main advantages.

1. Clarity
Frequentist statistics often relies on complex and counterintuitive concepts such as p-values, confidence intervals, and null hypotheses. Bayesian statistics, on the other hand, gives a straightforward result: the probability that variant A performs better than variant B. Even better, these probabilities can be translated directly into business terms, such as the probability that variant A will generate more profit. This makes communication with stakeholders much easier.

2. Ease of use
Bayesian A/B testing allows you to check progress at any time without compromising validity. This means tests can be stopped early once the results are strong enough to make a confident decision.

3. Smaller sample sizes
In some cases, you can reduce the amount of data required by incorporating prior knowledge or existing insights about the variations being tested.

What is Bayesian A/B Testing?

Bayesian A/B testing is a statistical method for comparing different versions of a website, app, or product by analysing experimental data. Unlike classical A/B testing, it uses Bayesian probability rather than the traditional frequentist approach. The result is easier to interpret and more actionable.

If your organisation finds traditional A/B testing confusing or difficult to explain, a Bayesian approach can make experimentation simpler, faster, and clearer.

How does it work?

How do we get started?

In Bayesian A/B testing, prior assumptions about the effectiveness of each variation are represented as probability distributions. As new data is collected, these distributions are updated using Bayes’ theorem to form posterior distributions, which reflect the updated beliefs about each version’s performance.

These posterior distributions are then used to make inferences such as the probability that one version performs better than another or to estimate the expected business impact of each option.

Whether you are new to A/B testing or already experienced but facing the limitations of classical methods, we can help you implement Bayesian A/B testing with confidence. We provide:

• Ready-to-use notebook templates and Python packages to support analysts and data scientists.

• A well-defined business process for Bayesian A/B testing with standardised inputs and outputs.

• Training and mentoring for data science teams and business decision-makers.