Group Sequential Testing vs. Bayesian: Choosing a Statistical Model for A/B Testing

Choosing the right statistical model means choosing the source of data you trust to support critical business decisions. When you need reliable data from A/B tests in order to make informed decisions, understanding your options is an important part of the process.

Among business leaders, 93% believe having the right type of decision intelligence can make or break the success of an organization—and the same applies to the success of your experimentation program.

So, what are the options out there, and what are their strengths and weaknesses? In this post, we cover two prominent methods: group sequential testing and Bayesian analysis. We’ll take a look at their respective advantages, limitations, and ideal use cases to help you decide which statistical model best suits your A/B testing needs. We’ll also highlight why group sequential testing, now available in SiteSpect’s new stats engine, might be the superior choice to increase the effectiveness of your A/B tests.

Group Sequential Testing

What It Is

Group sequential testing is an advanced statistical model that allows for periodic analysis of data at predefined points throughout an experiment. Unlike traditional fixed horizon testing, which evaluates results only at the end, this framework enables earlier decision-making by assessing data intermittently.

With SiteSpect, you gain access to a visual chart report displaying sequential results for your KPI within your campaign analytics. The advantage of access to the ongoing data at every checkpoint is that you can track developing patterns over time and quickly identify winning and losing variations.

Power

One of the distinct benefits of group sequential testing is its statistical power. This means that this form of data collection is more sensitive to differences in results and enables your team to collect less data to reach a clear and decisive outcome. By conducting interim analyses, your team can stop tests early if there is sufficient evidence to draw a conclusion—which saves a lot of time and resources. This statistical model reduces the likelihood of wasting resources on ineffective variations and allows for quicker optimization of the user experience.

Adaptability

Group sequential testing is highly adaptable to changing conditions. The test plan is based on visits in the campaign and not time intervals, meaning the test plan inherently adapts to changing traffic volume. It mitigates the risks associated with temporal effects, such as seasonality or reactive markets, by incorporating multiple checkpoints throughout the testing period. This adaptability ensures that tests remain relevant and accurate despite interference from external influences.

Ease of Use

While more complex than fixed-horizon testing, group sequential testing can be user-friendly with the right tools. Platforms like SiteSpect provide structured frameworks that guide users through setting up test plans and checkpoints, making it accessible even for those without extensive statistical backgrounds. The new stats engine from SiteSpect offers built-in group sequential testing to streamline this process, making it easier than ever to implement and benefit from this powerful statistical model with just a few inputs.

When creating your test plan, you’ll estimate weekly traffic to your campaign, enter the baseline conversion rate for your KPI, and determine the minimum detectable effect (MDE) you want to measure.

The expected weekly visits should reflect the specific audience and pages targeted, as this impacts the campaign’s duration but not the total visits required. The baseline conversion rate is crucial—lower rates require more visits to reach a conclusion, so consider KPIs closely related to the change you’re testing. The MDE represents the percentage improvement you’re trying to reliably detect, with smaller MDEs requiring more traffic. SiteSpect will calculate the total visits needed and schedule checkpoints at 10%, 25%, 50%, 75%, and 100% of this target. Once finalized, applying the plan will save it to your campaign.

Remember, allowing your campaign to run for at least two weeks ensures a representative sample, especially with high-traffic or high-conversion KPIs.

Ideal Use Cases

This statistical model is ideal for scenarios where insights are critical, with the potential to lead to major cost reductions or boosts in revenue. It is particularly useful when the cost of running a test is high, and quick optimization can lead to significant resource savings. SiteSpect’s new stats engine is designed to maximize these benefits, offering a seamless experience for users looking to optimize their A/B testing strategies efficiently.

Bayesian Analysis

What It Is

Bayesian analysis is a statistical model that incorporates prior knowledge along with current data to update the probability of a hypothesis being true. Unlike frequentist methods, which rely solely on observed data, Bayesian statistics draw on prior beliefs to inform the analysis.

Power

The Bayesian statistical model offers statistical power by providing a probabilistic framework that accounts for uncertainty and variability in the data. This approach allows for nuanced interpretations and can be powerful, but only when prior information is available and reliable.

Adaptability

Bayesian analysis continuously updates the probability of supporting a hypothesis as new data comes in. This real-time adaptability makes it well-suited for dynamic environments where conditions can change rapidly.

Ease of Use

While Bayesian methods can be computationally intensive and require a good understanding of prior distributions, modern software tools have made these techniques more accessible. However, the need for specifying prior beliefs can be a barrier for some users who lack historical evidence, leaving them to turn instead to guesswork when applying Bayesian methods in a practical scenario.

Ideal Use Cases

Bayesian analysis is ideal for scenarios where decisions need to be made quickly, such as testing short-lived content like news headlines or articles that only remain relevant for a brief period. When immediate insights are crucial, Bayesian methods can provide actionable results within hours, making it a strong fit for fast-paced environments where waiting days or weeks for data is not an option.

Statistical Models Applied to A/B Testing

Pros

Both group sequential testing and Bayesian analysis have unique advantages as statistical models when applied to A/B testing. Group sequential testing allows you to better plan and reprioritize your tests with tools that guide you through the process. It also leads to strong confidence in your results. By monitoring checkpoints, you can complete tests earlier, running them only as long as needed for faster, more efficient experimentation. Bayesian methods offer a probabilistic view of results, incorporating prior knowledge to enhance data analysis and decision-making.

Cons

Group sequential testing requires careful planning and setup, and it comes with increased complexity. However, with SiteSpect’s newly implemented stats engine, this complexity is substantially reduced by a user-friendly interface and straightforward process. The Bayesian statistical model, while powerful, can be hindered by computational demands and the subjective nature of choosing prior data.

Choosing a Statistical Model

The choice between group sequential testing and Bayesian analysis depends on your specific needs and resources. If you require quick, adaptive testing with multiple checkpoints, group sequential testing may be the better choice. If you have reliable prior information and need a flexible, probabilistic approach, Bayesian analysis could be more suitable.

Final Thoughts

Both group sequential testing and Bayesian models offer powerful tools for A/B testing. Understanding their strengths, weaknesses, and ideal use cases will help you choose the best statistical model for your needs. However, with the introduction of SiteSpect’s new stats engine, group sequential testing becomes an even more compelling choice, providing reliable data interpretation and quick optimization to drive business success.

Ready to enhance your A/B testing with an advanced statistical model? Request a demo today to see how group sequential testing can help you achieve reliable, data-driven results.