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Intuition
A detective doesn't dust every surface in the city for fingerprints. They form a theory, "the butler did it", and then go looking for the one piece of evidence that would confirm or destroy it. Hypothesis-driven problem solving is the same move: instead of analyzing everything, you commit to a likely answer early and aim your limited time at testing it.
This is what separates a consultant from a data dump. Anyone can list ten things to look at. A consultant says which one probably matters and why.
Framework
State a hypothesis early. "My leading hypothesis is that the profit drop is a cost problem, specifically rising logistics."
Make it testable. A good hypothesis names the data that would prove it right or wrong.
Test, then update. Go to the branch, get the number, and say what it means. Confirmed → go deeper. Killed → pivot, out loud.
Stay disciplined, not stubborn. Hold your view confidently but drop it the instant evidence says otherwise.
Sort it, no overlaps
Testable or too vague?
Tap each card to cycle it into a bucket, then grade the board.
1 · Testable hypothesis2 · Too vague to test
Worked Example
A retailer's margins are shrinking. Weak approach: "Let me look at revenue, costs, competition, pricing, and the market." Strong approach: "Margins, not revenue, are the issue, so my hypothesis is that costs rose faster than price. Within costs, I'd bet on input prices given recent commodity moves. Can I see cost of goods over the last three years?" If COGS is flat, you say so and pivot to price erosion. You've spent your minutes like a detective, not a vacuum cleaner.
The same muscle works in operations. A hospital ER's average wait time doubled. Vacuum-cleaner version: "Let me map the entire patient journey." Detective version: "Waits spike when arrivals outpace staffed capacity, and my hypothesis is the gap sits at the evening peak, can I see arrivals by hour against the staffing roster?" If the evening gap isn't there, you pivot to treatment time per patient. Either way, one chart settles it, that's a hypothesis earning its keep outside a profit case.
Pause & think
Your stated hypothesis was "input costs rose faster than price." The exhibit arrives: COGS dead flat for three years. What is the exact next sentence out of your mouth?
In the room
A hypothesis only counts if you say it like a commitment, with its kill condition attached: "My leading hypothesis is that the drop is cost-driven, and if cost of goods turns out flat, I'm wrong and this becomes a pricing story." Naming the kill condition is the part most candidates skip, and it is the part that sounds like a consultant. When data confirms: "That supports it, let me go one level deeper into logistics." When data kills it: "That rules out costs, so this is likely price erosion, can I see average price by segment?" At the pivot moment the interviewer is scoring exactly one thing: does evidence change your mind faster than ego defends it?
Pitfalls
Refusing to commit, "I'd need to see all the data" reads as no point of view.
Clinging to a dead hypothesis after the data killed it.
A hypothesis so vague ("something is wrong with the business") that no data could test it.
Take it with you
A one-card recap of this lesson, download it for your notes or share it with someone prepping alongside you.
Now train it
Reading this lesson was the easy half. These take the same move live:
