tester-experiments

Run Braze A/B tests by segmenting users into control and treatment cohorts using random_bucket_number ranges, such as control 0–4999 versus treatment 5000–9999. This requires a declared primary metric, a fixed analysis window, and a sample-size plan for valid comparisons.

Calculating sample size for Braze feature flag experiments requires a pre-defined primary conversion metric and a fixed experiment duration. Applying statistical power calculations ensures your controlled rollout groups are large enough to detect measurable engagement lift.

You can use Braze feature flags to gate functionality, define variants, and measure downstream conversion events. This controlled comparison approach guides rollout decisions by determining whether feature changes produce measurable improvements in user engagement.

The best way to ensure statistical integrity in Canvas experiments is to pre-register primary metrics, fix experiment durations, monitor guardrail metrics, and apply multiple-comparison corrections when needed. Sufficient sample size planning prevents inconclusive messaging variant tests.

Braze experiments often lack measurable engagement lift due to insufficient sample size for statistical power, unfixed analysis windows, or undefined primary metrics. Without deterministic random_bucket_number segmentation and guardrail metrics, control and treatment cohort comparisons remain invalid.