configuring-experiment-rollout

Configuring experiment rollout

This skill answers: Who sees what variant?

Recommended approach: equal split + adjust rollout percentage

In most cases, experiments work best with an equal split. If you want to limit exposure to the test variant, adjust the rollout percentage instead.

Why equal splits are better:

• Equal splits maximize statistical power — each variant has the same sample size
• Equal splits balance traffic and thus reach significance faster
• Increasing user exposure throughout the experiment through increasing rollout is clean (changing split mid-experiment can cause users to switch variants, which is bad for user experience and data quality)

Always default to an equal split unless the user explicitly requests otherwise.

When an uneven split is required

Uneven splits combined with the default "Exclude multivariate users" handling can introduce bias. If the experiment observes multi-variant users (users exposed to more than one variant) then those are dropped asymmetrically — the smaller variant loses a larger fraction of its assignments. If those users behave differently from the rest, the smaller variant's metrics will be skewed.

The right mitigation depends on experiment state:

1. Pre-launch, or live but with few exposures so far — use an equal split and reduce the overall rollout. Achieves the same test-variant exposure without the bias and preserves statistical power. See the disambiguation question below.
2. Live experiment with significant exposures — switch multivariate handling to "First seen variant". Changing the split mid-run reassigns users across variants (anti-pattern; see "Changing rollout on a running experiment" below). Switching handling instead keeps everyone in their original variant and avoids the asymmetric exclusion. See configuring-experiment-analytics for how to set this. Note that "first seen" handling can introduce other biases, but it's preferable to mid-run reassignment.