ab-test-setup

Sample size calculation requires defining your primary metric, expected conversion rate lift, and significance threshold. This Skill provides structured estimation workflows to ensure your A/B test gathers sufficient data for statistically valid results before analysis.

A structured A/B test hypothesis predicts the causal impact of a single-variable change on a specific metric. This Skill enforces a formal framework to define clear, testable predictions for landing pages, checkout flows, and pricing pages.

A/B tests compare a single variant against control, A/B/n tests multiple variants, and MVT evaluates multiple variables simultaneously. This Skill guides you on when to use each test type based on your experiment's scope and traffic constraints.

Yes, defining primary, secondary, and guardrail metrics before execution prevents unintended negative impacts. This Skill helps you pre-define these metrics to monitor performance and maintain statistical rigor during pricing page tests.

Traffic allocation for checkout flow experiments depends on your sample size requirements and risk tolerance. This Skill provides guidance on splitting visitors and choosing between client-side and server-side implementation approaches.

Statistical validity can be compromised by poor variant design, multiple comparisons, or early peeking at results. This Skill enforces a formal workflow for structured results interpretation and standardized documentation to prevent these issues.