Startup Idea Validation
Systematic validation for testing ideas before building: define hypotheses, collect evidence, score the opportunity, and make a decision you can defend.
Operating Principles (2026) Prefer decisions over inventories: each dimension ends with GO / CONDITIONAL / PIVOT / NO-GO and a next action. Separate evidence quality from confidence: weak evidence cannot justify a high score. Pre-register thresholds and stop rules before running experiments (avoid moving goalposts). Validate willingness-to-pay and time-to-value early (price is part of the product). Calibrate thresholds to the target outcome (venture-scale vs cash-flow business) and business model (B2B SaaS, B2C, marketplace, services). Stay safe and ethical: no misrepresentation, respect ToS, and handle customer data with minimization and retention limits. Intake Checklist (Ask First) One-sentence idea + target user + job-to-be-done Business model: B2B/B2C, SaaS/usage-based/marketplace/services, ACV/ARPU range Geography, constraints (regulated domain, procurement/security requirements, data access) Target outcome: venture-scale, profitable small business, or thesis-driven R&D Current evidence: interviews, pilots, pre-sales, traffic, competitor list, pricing assumptions Choose the Right Output If the user asks… Produce… Use… “Validate this idea” / “Is this worth building?” 9-dimension scorecard + verdict validation-scorecard.md, go-no-go-decision.md “What’s the riskiest assumption?” RAT + test plan riskiest-assumption-test.md, validation-experiment-planner.md “Test my hypothesis” Hypothesis canvas + experiment design hypothesis-canvas.md, hypothesis-testing-guide.md “Market size for X” TAM/SAM/SOM sizing + assumptions table market-sizing-worksheet.md, market-sizing-patterns.md “Can this be profitable / what’s my runway?” Unit economics + runway + scenarios financial-modeling-calculator.md “Should I build X or Y?” Comparative scorecard + decision memo validation-scorecard.md, go-no-go-decision.md Workflow Clarify the target outcome and business model; set default thresholds accordingly. Identify the RAT (the assumption that kills the business if wrong). Plan the validation ladder: interviews -> smoke test -> concierge/WoZ -> paid pilot. Run the cheapest falsifiable test first; pre-register PASS/FAIL thresholds and stop rules. Score all 9 dimensions using evidence; downgrade scores when evidence is weak. Produce a decision memo: verdict, why, what would change the decision, and the next smallest reversible step. 9-Dimension Scorecard Dimension Weight What it measures Problem severity 15% Urgency, cost of inaction, current workarounds Market size 12% Sufficient demand for the target outcome Market timing 10% Clear “why now” and tailwinds Competitive moat 12% Defensibility over time Unit economics 15% Profit path (incl. payback and margins) Founder-market fit 8% Access, expertise, and execution capability Technical feasibility 10% Buildability, dependencies, constraints GTM clarity 10% ICP, channels, motion, first customers Risk profile 8% What can kill it and likelihood
Verdict thresholds (default):
80–100: GO 60–79: CONDITIONAL (validate RAT first) 40–59: PIVOT <40: NO-GO
Deep scoring rubrics and calibration live in validation-methodology.md.
Evidence Rules Strong evidence is behavioral commitment with cost (time, money, switching, access); weak evidence is opinions and hypotheticals. Triangulate important claims across at least two sources (especially market sizing and competitor state). Keep an evidence trail: link + capture month; separate “fact” vs “assumption”. Validation Ladder (Default) Step Goal Strong signal Interviews Validate the problem and context Repeated pain with real workarounds and spend Smoke test Validate demand Qualified conversion with price shown Concierge/WoZ Validate workflow value Users complete the job and return Paid pilot Validate willingness-to-pay Paid, renewed, or expanded AI / Automation Notes (2026)
If the idea depends on AI (agents, copilots, automation), validate these explicitly:
Data rights and access: can you legally and reliably access required data? Reliability: define success metrics, failure modes, and human fallback; validate on real workflows. Cost-to-serve: model inference + retrieval + human-in-the-loop costs in assets/financial-modeling-calculator.md.
See hypothesis-testing-guide.md for AI-specific experiment patterns.
Integration Points Receives From startup-review-mining - Pain point evidence startup-trend-prediction - Market timing inputs startup-competitive-analysis - Competitor landscape Feeds Into router-startup - Startup decision routing product-management - Validated requirements and roadmap inputs startup-business-models - Monetization and packaging decisions Resources Resource Purpose validation-methodology.md Scoring rubrics and calibration hypothesis-testing-guide.md Experiment design and RAT workflows market-sizing-patterns.md TAM/SAM/SOM methods and pitfalls moat-assessment-framework.md Defensibility analysis Templates Template Purpose validation-scorecard.md Full 9-dimension scoring go-no-go-decision.md Decision memo format hypothesis-canvas.md Hypothesis definition validation-experiment-planner.md Experiment planning + thresholds riskiest-assumption-test.md RAT identification and test design market-sizing-worksheet.md Sizing worksheet financial-modeling-calculator.md Runway + scenarios + unit economics Data File Purpose sources.json Curated validation resources