What is Analysis of Competing Hypotheses (ACH)?

ACH is a structured analytical technique developed by Richards Heuer at the CIA to reduce cognitive bias in analysis. Applied to product management, it evaluates a hypothesis by scoring each piece of evidence against each assumption in isolation — preventing the analyst from unconsciously weighting evidence toward their preferred conclusion.

How does ACH reduce confirmation bias in product decisions?

By making each scoring judgment in isolation — one piece of evidence against one assumption at a time — without looking at adjacent scores or running totals. This breaks the pattern where analysts unconsciously weight evidence that supports their existing belief more heavily than evidence that contradicts it.

When should I use ACH instead of simpler analysis?

ACH is most valuable for high-stakes decisions with conflicting evidence, when the team has strong prior beliefs about the outcome, or when you need a transparent and auditable decision trail. For simple decisions with clear evidence, the overhead is not worth it.

Skill

Analysis of competing hypotheses

Run Analysis of competing hypotheses

Test a hypothesis with Analysis of Competing Hypotheses: gather evidence, score against all assumptions in isolation, produce a verdict matrix and report.

Use cases

“Weigh all the evidence against this bet”

Requires

Hypothesis

What You Provide

Hypothesis

What You Get

ACH Matrix
Experiment Report

How It Works

1
Load the hypothesis and assumptions
Identify the hypothesis being tested and all its associated assumptions across risk dimensions — value, usability, feasibility, and viability.
2
Build the isolation matrix
Create an empty ACH matrix with each assumption as a row. The matrix structure enforces the core mechanic: one evidence piece scored against one assumption at a time.
3
Gather evidence assumption-blind
Search the workspace and web for evidence relevant to the hypothesis. Evidence gathering is driven by relevance to the hypothesis, not by what helps any individual assumption.
4
Score in isolation and compute rankings
For each piece of evidence, independently assess whether it supports or contradicts each assumption without looking at adjacent scores. Then compute diagnosticity and rank hypotheses by how well they survive the full evidence set.

Why informal evidence reviews produce biased conclusions

When you evaluate evidence informally, confirmation bias shapes every judgment. You weight supporting evidence more heavily, discount contradicting evidence, and the hypothesis you already believe ends up winning regardless of what the data actually says. Analysis of Competing Hypotheses breaks this by making each judgment in isolation: given this one piece of evidence, which assumptions does it support and which does it contradict? The matrix is filled in assumption-blind, and the scoring is computed mechanically from the evidence rather than from prior beliefs.

Who it's for

Most valuable when you have at least five to ten pieces of evidence and three or more assumptions to evaluate. ACH adds overhead for simple decisions; it pays off for complex, high-stakes hypotheses where the team biases are most likely to mislead.

Product Manager

Product Strategist

Founder

Research-Oriented PM

Frequently asked questions

What is Analysis of Competing Hypotheses (ACH)?: ACH is a structured analytical technique developed by Richards Heuer at the CIA to reduce cognitive bias in analysis. Applied to product management, it evaluates a hypothesis by scoring each piece of evidence against each assumption in isolation — preventing the analyst from unconsciously weighting evidence toward their preferred conclusion.
How does ACH reduce confirmation bias in product decisions?: By making each scoring judgment in isolation — one piece of evidence against one assumption at a time — without looking at adjacent scores or running totals. This breaks the pattern where analysts unconsciously weight evidence that supports their existing belief more heavily than evidence that contradicts it.
When should I use ACH instead of simpler analysis?: ACH is most valuable for high-stakes decisions with conflicting evidence, when the team has strong prior beliefs about the outcome, or when you need a transparent and auditable decision trail. For simple decisions with clear evidence, the overhead is not worth it.