Pre-Experiment Power Sizing & Variance Modeling

Eliminating Underpowered Experiments Before Writing Code

Running an underpowered experiment is among the most costly errors in digital product development. When an experiment lacks adequate sample volume or statistical power, product teams face two failure modes: either terminating prematurely with false negative conclusions (abandoning genuine improvements), or artificially inflating effect sizes through small-sample variance spikes.

Flow Harbor Point’s Pre-Experiment Power Sizing & Variance Modeling engagement provides your engineering and product teams with an empirical, mathematically sound testing blueprint prior to code deployment.

What the Sizing Engagement Delivers

1. Empirical Historical Variance Analysis

We ingest your historical user activity, transaction, and conversion distributions across 30 to 90 days. We analyze metric variance, skewness, seasonality, day-of-week cyclicality, and zero-inflation patterns (common in purchase frequency metrics).

2. Precise Statistical Power Calculations

Using exact formulas tailored to your metric structure (binary conversion, continuous continuous revenue, ratio metrics, or count data), we establish the trade-off curve between:

• Type I Error Rate ((\alpha)): Typically set at 5% (two-tailed).
• Statistical Power ((1 - \beta)): Modeled across 80%, 85%, and 90% thresholds.
• Minimum Detectable Effect (MDE): The smallest relative lift your test will reliably detect given your traffic constraints.

3. Allocation Strategy & Multi-Arm Sizing

If your test evaluates multiple variants (e.g. Control vs Variant A vs Variant B), we design optimal allocation ratios (e.g., Dunnett’s test allocation) to maximize statistical efficiency while minimizing traffic exposure to suboptimal variants.

Deliverables

1. Experiment Sizing Blueprint (PDF/Markdown): Comprehensive documentation outlining minimum required sample sizes per arm, recommended runtime in full-week increments to account for weekly seasonality, and variance reduction feasibility.
2. Interactive Sensitivity Curve Workbook: Customized computation parameters allowing your team to explore runtime scenarios across varying traffic forecasts.
3. Hypothesis Formulation Review: Methodological consultation on primary metric selection, ensuring the chosen conversion event occurs close enough to the intervention to avoid metric dilution.

Engagement Details & Intake

• Turnaround: 2 to 3 business days from historical data receipt.
• Lead Consultant: Danai Siriporn & Senior Analytics Staff.
• Next Step: Submit a Pre-Test Briefing or check Pricing Guidance.