Holdout testing explained: validating experiment results

In the world of product development, making changes that truly resonate with users is both an art and a science. We've all been there: rolling out a new feature that seems promising, only to wonder if its initial success will stand the test of time.

That's where holdout testing comes into play. It goes beyond traditional methods to help you understand the long-term impact of your product changes. In this blog, we'll dive into what holdout testing is, why it's essential, and how you can implement it effectively to make smarter decisions.

Understanding holdout testing

Holdout testing is like setting aside a secret group of users who don't receive your latest updates. By excluding a small segment from new changes, you can compare their behavior to those who did get the updates. This approach helps you spot any negative side effects or user fatigue that might not be immediately obvious.

Unlike traditional A/B testing, which focuses on immediate reactions, holdout testing looks at the bigger picture over weeks or even months. While A/B tests provide quick insights into short-term metrics, holdout tests reveal the true sustained impact of your changes.

At Statsig, we believe holdout testing is crucial for validating experiment results and ensuring your changes have lasting positive effects. Without it, you might overestimate the cumulative impact of your experiments due to novelty effects, interactions between experiments, or statistical quirks. By comparing metrics between the holdout group and those exposed to changes, you gain valuable insights into the real influence of your product decisions.

Implementing holdout tests also helps you catch potential interactions between experiments and mitigates the risk of overestimating individual impacts. It accounts for novelty effects and short-term biases, allowing you to optimize based on data that reflects long-term success.

Implementing holdout tests effectively

So, how do you set up holdout tests the right way? It starts with creating three groups:

• Control group: Users who see no changes.

• Test group: Users who experience the updates.

• Holdout group: Users who remain untouched.

Using feature flags is a smart move—they help maintain consistent user experiences and prevent accidental exposure.

To keep your tests clean, it's important to avoid data leakage between groups. Regularly monitoring your metrics lets you spot trends early and validate your findings with solid statistical analysis. And don't forget to iterate and refine your experiments based on what you learn from your holdout tests.

When interpreting results, it's all about understanding discrepancies between individual experiment outcomes and the overall impact. Techniques like Monte Carlo simulations can give you more accurate estimations. Balancing short-term gains with long-term effects helps you make informed decisions that benefit your product in the long run.

Watch out for technical debt during holdout periods and handle any critical updates with care. Integrating holdout testing into your overall experimentation strategy means clearly communicating its purpose and implications to everyone involved. And, of course, keep a close eye on your holdout group to maintain test integrity.

Analyzing and interpreting holdout test results

Now, let's talk about making sense of your holdout test results. The key is comparing important metrics between your holdout group and the group exposed to changes over time. This comparison helps you uncover the true impact of your product updates, considering factors like novelty effects and interactions between experiments. Using statistical methods like stratified sampling ensures your holdout group accurately represents your overall user base, giving you more reliable insights.

When interpreting the results, consider a few things:

• Long-term impact: Holdout tests highlight how your changes affect metrics like engagement, retention, and revenue over time.

• Unintended consequences: Comparing the holdout and exposed groups can help you spot any negative side effects or signs of user fatigue.

• Experiment validation: Holdout testing validates the cumulative impact of your experiments and can address discrepancies that pop up from individual experiment results.

To get the most out of your holdout tests, collaborate with your data scientists, product managers, and engineers. Sharing insights leads to better, informed decisions. And don't forget to keep stakeholders in the loop—regularly communicating the purpose and outcomes of holdout testing builds trust in your experimentation process. By weaving holdout test insights into your product development, you foster a data-driven culture and reduce risks when rolling out new features.

Integrating holdout testing into your experimentation strategy

Bringing holdout testing into your experimentation strategy is a game-changer for making data-driven decisions. By leveraging insights from holdout tests, you can validate your product changes and steer your roadmap in the right direction. Keeping stakeholders informed about the purpose and results of holdout tests enhances trust and transparency.

At Statsig, we know that fostering a data-driven culture through consistent holdout testing practices minimizes risks when launching new features. It builds confidence in your experimentation processes and paves the way for future improvements. Plus, holdout tests can help you identify which teams are driving the most value, guiding strategic decisions.

Of course, there are challenges. Not every program is ready for holdouts—they require a stable product experience for control groups and a sufficient sample size. Holdout tests might not be the best fit when targeting is unstable or if you can't keep the product experience consistent.

To make holdout testing work for you, consider these best practices:

• Maintain consistent user segmentation: Keep your groups well-defined and stable.

• Use feature flags: They help prevent accidental exposure to changes.

• Compare key metrics: Regularly look at the data between holdout and exposed groups.

• Employ techniques like stratified sampling: Ensure your holdout group truly represents your user base.

By recognizing these challenges and adopting these practices, you can effectively integrate holdout testing into your experimentation strategy and reap the benefits.

Closing thoughts

Holdout testing is a powerful way to understand the true long-term impact of your product changes. By going beyond traditional A/B testing, it helps you make smarter, data-driven decisions that benefit both your users and your business. At Statsig, we're big believers in the value of holdout testing and how it can elevate your experimentation strategy.

If you're interested in learning more, check out our resources on holdout testing and how Statsig can help you implement it effectively. Hope you find this useful!