Stratified Sampling for A/B Test Variance Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multivariate testing for web page variations faces challenges in isolating the variance caused by the test experience, as other forms of variance, such as user characteristics, can significantly impact the results, leading to less accurate and meaningful evaluations.
Innovation Solution
The implementation of stratified sampling, where pseudorandom characters are generated for each permutation of stratification factors characterizing users, ensuring an equal distribution of users across variations, thereby minimizing non-test related variance and providing a more accurate evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional random sampling is used in A/B testing, then user assignment is simple and unbiased, but additional variance from user characteristics reduces measurement precision
Solution Approach 1:
The user population is segmented into distinct strata based on characteristics such as subscription status, device type, or usage patterns. Each stratum is then sampled independently to ensure proportional representation in the A/B test, reducing variance from user characteristic differences while maintaining systematic organization rather than simple randomization.
2Measurement precision
If stratified sampling is implemented to reduce variance, then measurement precision improves, but the complexity of user assignment and tracking increases
Solution Approach 1:
User strata are determined and pseudorandom assignment keys are generated in advance before the A/B test begins. This preliminary organization of users into strata with pre-computed assignment probabilities simplifies the actual testing phase, as the system only needs to lookup and apply pre-determined assignments rather than performing complex real-time stratified sampling calculations.
3Measurement precision
If user characteristics are considered in assignment, then evaluation accuracy improves, but the simplicity of implementation is lost
Solution Approach 1:
The system automatically determines user strata based on stored user characteristics and applies pseudorandom assignment without requiring manual intervention. The multivariate testing component autonomously queries user data, assigns users to appropriate strata, generates pseudorandom characters for assignment, and tracks results, making the complex stratified sampling process self-executing rather than requiring manual setup or management.
Data Source
AI summary
Embodiments provide techniques for testing a plurality of variations of a user experience, where each of the plurality of variations is distinct from other variations in the plurality of variations. For each permutation of a plurality of stratification factors that characterize users of the online site, embodiments generate a respective set of pseudorandom numbers. Additionally, user requests are assigned to variations in the plurality of variations, based on the generated sets of pseudorandom numbers. Embodiments also track user retention information relating to the user requests. At least one of the plurality of variations is rated, based on the user retention information.