A/B Test Calculator - PMToolKit

Transform your A/B testing workflow with PM Tools - the comprehensive statistical assistant designed specifically for Product Managers. 🎯 What PM Tools Does: PM Tools brings enterprise-level statistical analysis directly to your browser, helping you design better experiments and make data-driven decisions with confidence. ⚡ Key Features: 📊 PRE-EXPERIMENT VALIDATION • Calculate required sample sizes instantly • Determine test duration based on your traffic • Validate if your hypothesis is testable • Get AI-powered insights on experiment design • Avoid common statistical pitfalls before they happen 📈 POST-EXPERIMENT ANALYSIS • Analyze results with statistical rigor • Understand significance and confidence intervals • Get plain-English explanations of your results • Receive actionable recommendations from AI • Export results in multiple formats (CSV, JSON) 🤖 AI-POWERED INSIGHTS (Optional) • Bring your own API key (Gemini or Claude) • Get contextual recommendations for your experiments • Understand complex statistical concepts in simple terms • Receive strategic guidance on next steps • Works perfectly without AI - all calculations run locally 💡 PERFECT FOR: • Product Managers running A/B tests • Growth teams optimizing conversion rates • UX researchers validating design changes • Marketing teams testing campaigns • Anyone who needs quick statistical calculations 🔒 PRIVACY FIRST • All calculations run locally in your browser • No data sent to external servers (except optional AI calls) • Your API keys are stored securely and never shared • Open source and transparent 🚀 WHY CHOOSE PM TOOLS? • No subscription fees - completely free • Works offline for all statistical calculations • Clean, minimalist interface • Instant results without page reloads • Built by PMs, for PMs Start making better data-driven decisions today. Install PM Tools and turn your browser into a powerful statistical consultant! Tags: a/b testing, statistics, calculator, product management, experiment design, conversion optimization, statistical significance, sample size, hypothesis testing