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.

A scientific, customer-centric framework for systematically improving conversion rates on websites and landing pages. This methodology guides users through funnel mapping, identifying core visitor objections, and conducting evidence-based A/B testing. It is essential for auditing underperforming funnels, optimizing checkout flows, and transforming general marketing hypotheses into measurable, statistically significant improvements.

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 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 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.

This detailed framework guides researchers in conducting rigorous and academically sound heterogeneity analysis for finance and economics manuscripts. It covers dimension prioritization, theoretical justification, sample size requirements, and statistical significance testing protocols (e.g., Chow test). Use this guide to strengthen the robustness of main findings and deepen the paper's contribution by identifying the specific conditions under which the effect operates.

A comprehensive final checklist for authors submitting manuscripts to top academic journals (e.g., JAP) via Editorial Manager. It ensures compliance with APA 7th edition style, full author anonymization for masked review, adherence to open science (TOP) standards (data/code availability), and statistical rigor. This guide helps prevent common submission failures before final upload.