Understanding sample ratios

Sample ratios are crucial in experiments. They represent the proportion of users allocated to each variation in your experiment. For instance, in a simple A/B test, you might split users 50/50 between two versions of a feature. This ensures each version gets a fair amount of traffic, making your results reliable.

When your sample ratios are balanced, your experiment results are more trustworthy. An imbalance can skew your data, leading to incorrect conclusions. Imagine testing a new feature where one variation receives significantly more users than the other. This imbalance can make it hard to tell if the feature's success or failure is due to the feature itself or the uneven traffic distribution.

Maintaining balanced sample ratios prevents bias in your experiment. Even slight differences in user allocation can impact your results. Here are some key reasons to keep your sample ratios balanced:

• Accurate Results: Balanced ratios ensure each user group is comparable.

• Reduced Bias: Prevents one variation from getting more favorable conditions.

• Valid Conclusions: Ensures your experiment findings are based on solid data.

To achieve balanced sample ratios, monitor your traffic distribution regularly. Automated tools can help detect imbalances early, allowing you to take corrective actions promptly. If you notice an imbalance, investigate potential causes like outdated SDKs or application behavior issues. Fixing these issues quickly helps maintain the integrity of your experiment.

What is a sample ratio mismatch (SRM)?

A sample ratio mismatch (SRM) occurs when the traffic distribution between experiment variations deviates from the expected allocation. This imbalance can indicate an issue with the experiment setup. You can learn more about SRM here.

Why is SRM important?

An SRM can happen for several reasons:

• Outdated SDKs: Older versions may cause incorrect event tracking. For more information on how to monitor your SDK versions, see the Statsig documentation.

• Event Deduplication: Duplicate events can skew traffic distribution. Learn more about deduplication here.

• Application Behavior: Certain behaviors can lead to uneven user allocation. For best practices in experiment setup, check out this guide.

The impact of an SRM on experiment results is significant. It can introduce bias, making your findings unreliable. This undermines the validity of your conclusions, potentially leading to incorrect decisions. For more on the methodology behind SRM checks, visit this page.

How to detect and fix SRMs

Methods to identify SRMs

Monitor traffic distribution: Regularly check if user allocation matches your expected ratios. Discrepancies often signal an SRM. For more details, you can refer to the SRM - Sample Ratio Mismatch documentation.

Use statistical tools: Implement tools to automatically flag imbalances. These tools provide early warnings, ensuring you address issues promptly. You might find it useful to read about SRM Checks for better understanding.

Corrective actions for SRMs

Update SDKs: Ensure you’re using the latest versions. Old SDKs frequently cause mismatches. Check out how to update your SDKs with the Statsig SDK documentation.

Check application behavior: Investigate if certain actions lead to uneven traffic. Adjust your experiment setup to mitigate these issues. For detailed guidance, see the Statsig documentation on setting up your first feature gate.

Practical examples of sample ratios

Example 1: A/B test imbalance

Scenario: You run a 50/50 A/B test but notice an SRM. Instead of a 50/50 split, you see a 60/40 distribution. This imbalance skews your results.

Steps to resolve:

• Check randomization: Ensure your randomization algorithm functions correctly.

• Update SDK: Make sure you’re using the most recent SDK version.

• Monitor traffic: Confirm even distribution of users throughout the test period.

Example 2: Event loss in experiments

Scenario: An experiment loses events due to user behavior. For instance, one variant redirects users quickly, resulting in event loss. This leads to an SRM.

Solutions:

• Adjust redirects: Ensure both variants have similar user engagement time.

• Improve event tracking: Utilize reliable event tracking methods.

• Analyze behavior: Identify and mitigate behaviors causing event loss.