Streaming Recommendation Engine Experimentation Plan

Role: Principal Experimentation Statistician for Media Platforms

Context

• Streaming Platform: {{streaming_platform_name}}
• Primary Target Metric: {{primary_metric}}
• Baseline Metric Rate: {{baseline_metric_rate}}
• Minimum Detectable Effect (MDE): {{minimum_detectable_effect}}
• Traffic Allocation: {{traffic_allocation_percentage}}
• Testing Horizon: {{testing_horizon_days}}

Task

Develop a comprehensive statistical experimentation plan for evaluating algorithmic recommendation variants on {{streaming_platform_name}}, establishing sample size calculations, variance reduction techniques, and decision rules to detect changes in {{primary_metric}}.

Method

1. Formulate exact null ($H_0$) and alternative ($H_1$) hypotheses for {{primary_metric}} across treatment and control variants.
2. Compute required sample size and statistical power (targeting $\beta = 0.20$ at $\alpha = 0.05$) based on {{baseline_metric_rate}} and {{minimum_detectable_effect}} over {{testing_horizon_days}}.
3. Specify variance reduction procedures such as CUPED using pre-experiment streaming engagement data to increase statistical precision.
4. Design randomization and stratification schemes to balance user cohorts across {{traffic_allocation_percentage}} allocation.
5. Define sequential testing boundaries or alpha-spending functions to prevent false positives from continuous monitoring.
6. Detail sample ratio mismatch (SRM) detection checks and automated guardrail metric thresholds.
7. Outline the post-test inferential framework, including confidence interval calculation and subgroup heterogeneity analysis.

Constraints

• MUST account for intra-user autocorrelation in time-spent and session count data.
• MUST include explicit early stopping criteria for both positive efficacy and negative guardrail breaches.
• MUST NOT recommend standard fixed-horizon p-value evaluation if continuous monitoring dashboards are used.
• Do not use generic testing terminology; all metrics and calculations must reflect media streaming consumption patterns.

Output format

Provide a structured experimentation plan containing:

1. Hypothesis & Power Formulation (with exact formulas and parameter assignments)
2. Variance Reduction & Stratification Architecture
3. Monitoring Protocols & SRM Safeguards
4. Decision Matrix & Inference Guidelines (under 600 words)

Self-review

• Confirm statistical power derivations accurately reflect {{minimum_detectable_effect}} and {{baseline_metric_rate}}.
• Verify all 6 input variables are deeply integrated into the methodological steps.
• Ensure sequential monitoring rules directly prevent alpha inflation.