A hold-out test is an experimentation method that measures the long-term impact of product changes. It involves setting aside a "hold-out" group, or control group, that is not exposed to the changes for an extended period, typically weeks or months after the main experiment concludes.

The hold-out group acts as a baseline, allowing you to compare the behavior of users who continue to experience the product change against those who do not. This comparison reveals any lasting effects, positive or negative, that the change may have on key metrics like engagement, retention, or revenue.

By isolating the hold-out group from the product change, you can:

• Assess the true, sustained impact of the change over time

• Identify any novelty effects that may inflate short-term metrics

• Detect delayed or gradual changes in user behavior

• Ensure the product change does not have unintended long-term consequences

Implementing a hold-out test requires a well-architected experimentation platform that can maintain the separation between the groups and track their respective metrics over an extended duration. The platform must also have sufficient statistical power to detect meaningful differences between the hold-out and exposed groups, which may be subtle or slow to emerge.

When executed properly, hold-out tests provide a rigorous and reliable assessment of a product change's long-term value. They help you make informed decisions about whether to fully roll out, iterate on, or roll back the change based on its demonstrated impact on your core metrics and overall business objectives.

Context and importance

Holdout testing is crucial for measuring the long-term impact of experiments. It ensures that changes don't have unintended negative consequences over time. Holdouts provide an unbiased way to measure the cumulative effect of multiple experiments.

Without holdout tests, the sum of individual experiment impacts may overestimate the actual impact. Factors like novelty effects, interactions between experiments, and statistical artifacts can skew results. Holdouts reveal the true aggregate impact of a team's experimentation program.

For mature experimentation programs, holdout tests are essential for accurately assessing their work. They help identify issues like underpowered tests or short-term novelty effects. Holdouts build trust in best practices and drive long-term program success.

Hold-out testing components

Hold-out testing involves three key components: a control group, a test group, and an extended time period. The control group consists of users who are not exposed to any product changes. In contrast, the test group includes users who experience the new product changes being tested.

Hold-out tests are unique because they are conducted over an extended time period, typically lasting weeks or months after the initial experiment ends. This extended duration allows for a more comprehensive evaluation of the long-term impact of product changes. By comparing the behavior and metrics of the control and test groups over this extended period, you can gain valuable insights into the true effectiveness of your product updates.

Benefits of hold-out testing

Hold-out testing offers several key benefits for product teams and organizations:

• Measuring long-term impact: Hold-out tests enable you to assess the sustained effects of product changes, beyond the initial novelty period.

• Identifying unintended consequences: By monitoring the control and test groups over an extended duration, you can detect any negative or unintended consequences that may arise from product updates.

• Validating experiment results: Hold-out testing serves as a powerful tool for validating the findings of shorter-term experiments, ensuring the reliability and robustness of your insights.

Implementing hold-out tests effectively

To successfully implement hold-out testing in your experimentation process, consider the following best practices:

• Define clear metrics: Establish well-defined, measurable metrics that align with your product goals and user behavior to accurately assess the impact of your changes.

• Ensure sufficient sample size: Allocate an adequate number of users to both the control and test groups to obtain statistically significant results.

• Monitor regularly: Continuously monitor the performance and behavior of the control and test groups throughout the extended time period to identify any emerging trends or anomalies.

• Analyze and iterate: Conduct thorough analysis of the hold-out test results and use the insights gained to inform future product iterations and experimentation strategies.

By incorporating hold-out testing into your experimentation framework, you can make data-driven decisions with greater confidence and optimize your product for long-term success. Embrace the power of hold-out tests to uncover valuable insights and drive meaningful improvements in your product's performance and user experience.

Implementing hold-out tests

To set up a hold-out test, create an experiment with three variants: control, test, and holdout. The control and test variants receive the existing and new experiences, respectively, while the holdout group is excluded from the change.

Integrate the experiment into your application code, ensuring the correct variant is shown to each user. Use feature flags to control the rollout and easily switch between variants.

Monitor key metrics like conversion rate, session duration, and user engagement throughout the experiment. Use statistical analysis to determine if the test variant outperforms the control group.

Based on the results, decide whether to fully roll out the change or continue iterating. If the test variant proves successful, gradually expose the holdout group to the new experience while monitoring for any adverse effects.

Hold-out tests provide a powerful way to measure the long-term impact of changes. By comparing the performance of the test and control groups against the isolated holdout group, you can assess the true effectiveness of your experiments.

Implementing hold-out tests requires careful planning and execution. Ensure your sample sizes are large enough to detect meaningful differences, and be prepared to run the experiment for an extended period.

When analyzing results, consider factors like seasonality, external events, and user segments. Look for consistent patterns across multiple metrics to gain a comprehensive understanding of the change's impact.

Remember, hold-out testing is not suitable for all experiments. It works best for changes that are expected to have a persistent, measurable effect on user behavior.

By incorporating hold-out tests into your experimentation strategy, you can make data-driven decisions with greater confidence. Embrace the power of rigorous testing to continuously improve your product and deliver value to your users.

When to use hold-out tests

Hold-out tests are essential for measuring the cumulative impact of multiple experiments. They provide an unbiased view of the long-term effects of product changes. Hold-out tests can also account for potential interactions between experiments that may not be apparent in individual tests.

To run effective hold-out tests, you need an adequate sample size and a stable product experience. The control group must remain consistent over time to avoid biased comparisons. Hold-out tests are unsuitable for experiments that randomize based on non-persistent identifiers or involve machine learning models with stale training data.

Hold-out test results can be surprising, often revealing that the bottom-up approach overestimates cumulative impact. If the hold-out impact is significantly lower than expected, it may indicate issues in the experimentation process, such as underpowered tests or novelty effects. Hold-out tests help build trust in best practices and identify high-value teams, driving long-term program success.