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FAQ

SDKs and APIs​

How does bucketing within the Statsig SDKs work?​

Bucketing in Statsig is deterministic. Given the same user object and the same state of the experiment or feature gate, Statsig always returns the same result, even when evaluated on different platforms (client or server). Here's how it works:

  1. Salt Creation: Each experiment or feature gate generates a unique salt.
  2. Hashing: The user identifier (e.g., userId, organizationId) is passed through a SHA256 hashing function, combined with the salt, which produces a large integer.
  3. Bucket Assignment: The large integer is then subjected to a modulus operation with 10000 (or 1000 for layers), assigning the user to a bucket.
  4. Bucket Determination: The result defines the specific bucket out of 10000 (or 1000 for layers) where the user is placed.

This process ensures a randomized but deterministic bucketing of users across different experiments or feature gates. The unique salt ensures that the same user can be assigned to different buckets in different experiments.

A lot of times people assume that we keep track of a list of all ids and what group they were assigned to for experiments, or which IDs passed a certain feature gate. While our data pipelines keep track of which users were exposed to which experiment variant in order to generate experiment results, we do not cache previous evaluations and maintain distributed evaluation state across client and server sdks. That model doesn't scale - we've even talked to customers who were using an implementation like that in the past, and were paying more for a Redis instance to maintain that state than they ended up paying to use Statsig instead.

For more details, check our open-source SDKs here.

Is it possible to add a layer to a running experiment?​

No. Once an experiment is started, you cannot change the layer. This restriction ensures the integrity of the experiment. We may support this feature in the future.

Why should I define parameters for my experiments instead of just getting the group?​

Defining parameters for experiments provides flexibility and speed in iteration. Many companies, such as Facebook, Uber, and Airbnb, follow this approach in their experimentation platforms because it allows:

  • Faster iteration (no code changes required for new experiments).
  • More flexible experiment designs.

For example:

Without Parameters (Group-based):​

if (otherExpEngine.getExperiment('button_color_test').getGroup() === 'Control') {
    color = 'BLACK';
} else if (otherExpEngine.getExperiment('button_color_test').getGroup() === 'Blue') {
    color = 'BLUE';
}

With Parameters (Statsig approach):​

color = statsig.getExperiment('button_color_test').getString('button_color', 'BLACK');

In the first case, adding a new color (e.g., "Green") requires a code change. In the second case, you can modify the experiment configuration without making a code change.

Why am I not seeing my exposures and custom events logged in Statsig?​

In short-lived processes (e.g., scripts or edge workers), the process may exit before the event queue flushes to Statsig. To ensure that exposures and events are logged, call statsig.flush() before the process exits.

For details on flushing, check the Node.js Server SDK documentation.

I don't see my client or server language listed. Can I still use Statsig?​

If none of our current SDKs meet your needs, please let us know via our Slack community!

Feature Flags​

When I change the rollout percentage of a rule on a feature gate, will users who passed continue to pass?​

Yes. If you increase the rollout percentage (e.g., from 10% to 20%), the original 10% will continue to pass, while an additional 10% will start passing. Reducing the percentage will restore the original 10%. To reshuffle users, you'll need to "resalt" the gate.

Statistics​

What statistical tests does Statsig use?​

Statsig uses a two-sample Z test for most experiments and Welch’s t-test for smaller sample sizes. These methods are industry-standard and have been validated through simulations and research.

How does Statsig handle low sample size?​

For small samples, we use Welch's t-test, which is more suitable for unequal group sizes or variances. We also support CUPED and winsorization to increase test power.

When should I use one-sided vs two-sided tests?​

Use a one-sided test if you're confident that you're only interested in movement in a specific direction. This increases the power of the test but sacrifices insight into secondary metrics.

Experimentation​

How can I get started with an A/B Test?​

If your feature isn't live yet, you can start an A/B test using a feature flag. If it's already in production, you can create an experiment. Results can be viewed in the Pulse Results tab.

Can I target my experiment to a subset of users (e.g., iOS users only)?​

Yes, you can. When setting up your experiment, select a Feature Flag with targeting rules. In this case, only iOS users would pass the gate.

Targeting iOS Users

Billing​

What counts as a billable event?​

Billable events occur when the Statsig SDK checks if a user is exposed to a feature flag or experiment or logs an event. Pre-computed metrics from data warehouses and custom metrics created from existing data also count as billable.

How do I manage my billable event volume?​

  1. Download a CSV from the Usage and Billing tab to review events contributing to your volume.
  2. Create a pivot table in Excel to identify the top event volume drivers.
  3. Admins receive proactive alerts at 25/50/75/100% of their contracted events.

Billing Usage

How Many Projects Can I Create With a Single Pro Subscription?​

Pro subscriptions are limited to one project each. You can create more projects within Statsig, but if you want to have access to pro features and 5M events, you will need to upgrade each project independently. You can read more about our Pro plans here.

Enterprise plans can support multiple projects. If you might be interest in this, contact us here!

Platform Usability​

When should I create a new project?​

Projects have distinct boundaries. Create a new project when you're managing a separate product with unique user IDs and metrics.

For example, if you have a marketing website (anonymous users) and a product (signed-in users), you may want to separate them. However, if you want to track success across both, you should manage them in the same project.