Ab Test Analysis

Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.

Invoke when the user needs to set up analytics, define telemetry events, establish KPIs, build dashboards, configure A/B testing, or implement data-driven design capabilities. Triggers on: "analytics", "telemetry", "KPIs", "metrics", "player data", "retention", "DAU", "dashboard", "A/B testing", "funnel analysis". Do NOT invoke for balance tuning (use game-balance-check) or economy design (use game-economy-designer). Part of the AlterLab GameForge collection.

Specifies requirements for an analytics dashboard including metrics, visualizations, filters, and data sources. Use when requesting dashboards from data teams, defining KPI tracking, or documenting reporting needs.

Evaluate acquisition channels using unit economics, customer quality, and scalability. Use when deciding whether to scale, test, or kill a growth channel.

Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, interpretation, decision-making, and the common failure modes that produce confidently wrong shipping decisions.