Test branches by forming specific, falsifiable hypotheses

Turn each branch into a testable hypothesis before gathering data.

Why it works

An issue tree branch is a hypothesis about the world — "the revenue decline is driven by customer churn, not lower acquisition." Treating it as a hypothesis before gathering data focuses data collection: you are looking for specific evidence that confirms or disconfirms the branch, not exploring generally. This structure prevents the drift where data collection continues indefinitely because no one stated what would count as enough evidence to close the branch.

How to do it

1. For each prioritized branch, write a one-sentence hypothesis: "Revenue declined because X happened in segment Y."
2. Specify in advance what data would confirm or falsify the hypothesis.
3. Gather the minimum data needed to resolve the branch one way or another.
4. Mark the branch confirmed, disconfirmed, or inconclusive, then move to the next.

Evidence

Hypothesis-driven analysis is McKinsey and consulting-industry standard practice. Hypothesis testing as a scientific method has extensive philosophical and practical justification; its application to business analysis is by extension. (mechanistic)

In business analysis, "falsifiable" is softer than in science — evidence is often ambiguous, and branch closure involves judgment. The practice reduces rather than eliminates ambiguity.

Sources

• Popper (1963), Conjectures and Refutations — on falsifiability as the criterion of scientific claims

Common mistake

Gathering data before forming the hypothesis, which means the data search is guided by what turns up rather than by what would resolve the question — this is exploratory, not analytical.