Skill UI

Guides teams through planning, designing, and running statistically sound A/B tests with hypothesis framing, sample sizing, metric selection, implementation strategies, and analysis checkpoints for systematic growth experimentation.

An expert guide for planning, designing, and implementing statistically rigorous A/B tests and product experiments. It covers core principles like formulating strong hypotheses, selecting primary/guardrail metrics, calculating sample sizes, and analyzing results to drive actionable business insights.

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.

This structured mode acts as an experiment design partner for Product Managers. It guides users through formulating testable, falsifiable hypotheses, designing rigorous A/B tests (including sample size, duration, and guardrails), and interpreting results honestly. It helps PMs move from vague beliefs to clear, data-backed decisions: Ship, Iterate, or Kill.

This guide provides a comprehensive framework for designing statistically rigorous A/B tests, split tests, and multivariate experiments. It covers everything from formulating strong hypotheses and calculating sample sizes to selecting appropriate primary, secondary, and guardrail metrics for various business contexts.

This expert framework guides users through the systematic process of conducting rigorous A/B tests and growth experiments. It covers all stages: formulating strong hypotheses, selecting appropriate metrics (primary, secondary, guardrail), calculating necessary sample sizes, and choosing the best implementation method (client-side vs. server-side). Use this when planning to compare two versions or build a systematic growth experimentation program.

This tool provides a comprehensive framework for designing rigorous A/B and multivariate experiments. It guides users through defining hypotheses, calculating statistically necessary sample sizes (handling absolute vs. relative MDE), designing variants, setting metrics, and creating detailed monitoring and stopping rules for CRO experimentation.

A comprehensive guide for structuring marketing research designs, particularly for journals like the Journal of Marketing (JM). It teaches researchers how to select the most appropriate method (e.g., field experiment, survey, panel data, qualitative study) that directly addresses a substantive, managerially relevant claim. The focus is on achieving both internal validity (establishing causality) and external validity (realism in the market), ensuring the method serves the core business insight rather than being an end in itself.

This guide helps researchers select and defend the appropriate empirical methodology for Operations Management (OM) studies. It covers diverse design types—including surveys, archival data analysis, behavioral experiments, field studies, and intervention-based research—ensuring the design is observation-grounded, matches the operational claim, and anticipates rigorous methodological checks.