A/B Test Calculator - PMToolKit

About

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